nils-herrmann/clean_scirepeval huggingface dataset:
Psychoactive constituents of cannabis and their clinical implications: a systematic review.

This systematic review aims to summarize current evidence on which naturally present cannabinoids contribute to cannabis psychoactivity, considering their reported concentrations and pharmacodynamics in humans.  Following PRISMA guidelines, papers published before March 2016 in Medline, Scopus-Elsevier, Scopus, ISI-Web of Knowledge and COCHRANE, and fulfilling established a-priori selection criteria have been included.  In 40 original papers, three naturally present cannabinoids (∆-9-Tetrahydrocannabinol, ∆-8-Tetrahydrocannabinol and Cannabinol) and one human metabolite (11-OH-THC) had clinical relevance. Of these, the metabolite produces the greatest psychoactive effects. Cannabidiol (CBD) is not psychoactive but plays a modulating role on cannabis psychoactive effects. The proportion of 9-THC in plant material is higher (up to 40%) than in other cannabinoids (up to 9%). Pharmacodynamic reports vary due to differences in methodological aspects (doses, administration route and volunteers' previous experience with cannabis).  Findings reveal that 9-THC contributes the most to cannabis psychoactivity. Due to lower psychoactive potency and smaller proportions in plant material, other psychoactive cannabinoids have a weak influence on cannabis final effects. Current lack of standard methodology hinders homogenized research on cannabis health effects. Working on a standard cannabis unit considering 9-THC is recommended.
Beatmap generator for Osu Game using machine learning approach

Rhythm game as one of the most-played game genres has its own attractiveness. Each song in the game gives its player new excitement to try another song or another difficulty level. However, behind every song being played is a lot of work. A beatmap should be created in order for a song to be played in the game. This paper presents an alternate way to create a beatmap that is considered playable for Osu Game utilizing beat and melody detection using machine learning approach and SVM as its learning method. The steps consists of notes detection and notes placement. Notes detection basically consists of features extraction from an audio file using DSP Java Library and learning process using Weka and LibSVM. However, detect the presence of notes only does not solve anything. The notes should be placed in the game using PRAAT and Note Placement Algorithm. From this process, a beatmap can be created from a song in about 3 minutes and the accuracy of the note detection is 86%.
Role of Deep LSTM Neural Networks and Wi-Fi Networks in Support of Occupancy Prediction in Smart Buildings

Knowing how many people occupy a building, and where they are located, is a key component of smart building services. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy. However, relatively simple sensor technology and control algorithms limit the effectiveness of smart building services. In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time. We use Wi-Fi datasets readily available in abundance for smart building services and train Auto Regression Integrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM) time series models. As a use case scenario of smart building services, these models allow forecasting of the number of people at a given time and location in 15, 30 and 60 minutes time intervals at building as well as Access Point (AP) level. For LSTM, we build our models in two ways: a separate model for every time scale, and a combined model for the three time scales. Our experiments show that LSTM combined model reduced the computational resources with respect to the number of neurons by 74.48 % for the AP level, and by 67.13 % for the building level. Further, the root mean square error (RMSE) was reduced by 88.2%–93.4% for LSTM in comparison to ARIMA for the building levels models and by 80.9 %–87% for the AP level models.
Infection control practices of laryngoscope blades: a review of the literature.

Current procedures for cleaning anesthesia airway equipment as assessed by the presence of visible and occult blood on laryngoscope blades and handles as labeled "ready for patient use" has been reported to be ineffective. Human immunodeficiency virus (HIV) and the hepatitis B virus (HBV) are 2 commonly seen pathogens that frequently are found in the healthcare setting. It has been shown that HBV can survive on a dry surface for at least 7 days and both HIV and HBV are transmitted via blood. The potential for cross-contamination from airway equipment to patient has been shown in several studies. To prevent further potential infections, it should be ascertained why anesthesia providers are not all using disposable laryngoscope blades. The purpose of this literature review is to determine the use and infection control practices of disposable laryngoscope blades. Their frequency of use, their evaluation of ease of use, and any complications encountered when using the disposable blade are reviewed, as well as the perceptions of anesthesia providers regarding disposable laryngoscope blades.
How Random Walks Can Help Tourism

On-line photo sharing services allow users to share their touristic experiences. Tourists can publish photos of interesting locations or monuments visited, and they can also share comments, annotations, and even the GPS traces of their visits. By analyzing such data, it is possible to turn colorful photos into metadata-rich trajectories through the points of interest present in a city. In this paper we propose a novel algorithm for the interactive generation of personalized recommendations of touristic places of interest based on the knowledge mined from photo albums and Wikipedia. The distinguishing features of our approach are multiple. First, the underlying recommendation model is built fully automatically in an unsupervised way and it can be easily extended with heterogeneous sources of information. Moreover, recommendations are personalized according to the places previously visited by the user. Finally, such personalized recommendations can be generated very efficiently even on-line from a mobile device.
Image Plane Sweep Volume Illumination

In recent years, many volumetric illumination models have been proposed, which have the potential to simulate advanced lighting effects and thus support improved image comprehension. Although volume ray-casting is widely accepted as the volume rendering technique which achieves the highest image quality, so far no volumetric illumination algorithm has been designed to be directly incorporated into the ray-casting process. In this paper we propose image plane sweep volume illumination (IPSVI), which allows the integration of advanced illumination effects into a GPU-based volume ray-caster by exploiting the plane sweep paradigm. Thus, we are able to reduce the problem complexity and achieve interactive frame rates, while supporting scattering as well as shadowing. Since all illumination computations are performed directly within a single rendering pass, IPSVI does not require any preprocessing nor does it need to store intermediate results within an illumination volume. It therefore has a significantly lower memory footprint than other techniques. This makes IPSVI directly applicable to large data sets. Furthermore, the integration into a GPU-based ray-caster allows for high image quality as well as improved rendering performance by exploiting early ray termination. This paper discusses the theory behind IPSVI, describes its implementation, demonstrates its visual results and provides performance measurements.
Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

Identifying regions of interest in an image has long been of great importance in a wide range of tasks, including place recognition. In this letter, we propose a novel attention mechanism with flexible context, which can be incorporated into existing feedforward network architecture to learn image representations for long-term place recognition. In particular, in order to focus on regions that contribute positively to place recognition, we introduce a multiscale context-flexible network to estimate the importance of each spatial region in the feature map. Our model is trained end-to-end for place recognition and can detect regions of interest of arbitrary shape. Extensive experiments have been conducted to verify the effectiveness of our approach and the results demonstrate that our model can achieve consistently better performance than the state of the art on standard benchmark datasets. Finally, we visualize the learned attention maps to generate insights into what attention the network has learned.
Flattened Butterfly Topology for On-Chip Networks

With the trend towards increasing number of cores in chip multiprocessors, the on-chip interconnect that connects the cores needs to scale efficiently. In this work, we propose the use of high-radix networks in on-chip interconnection net- works and describe how the flattened butterfly topology can be mapped to on-chip networks. By using high-radix routers to reduce the diameter of the network, the flattened butterfly offers lower latency and energy consumption than conven- tional on-chip topologies. In addition, by exploiting the two dimensional planar VLSI layout, the on-chip flattened but- terfly can exploit the bypass channels such that non-minimal routing can be used with minimal impact on latency and en- ergy consumption. We evaluate the flattened butterfly and compare it to alternate on-chip topologies using synthetic traffic patterns and traces and show that the flattened but- terfly can increase throughput by up to 50% compared to a concentrated mesh and reduce latency by 28% while re- ducing the power consumption by 38% compared to a mesh network.
Game Theory and Strategy

The course will be offered annually beginning in the Spring 2011 semester. Points and prerequisites: The course will carry four points. The prerequisites for this course are Economic Principles II (V31.0002) and Calculus I (V63.0121). The lectures will focus mainly on conceptual material and applications.  The course will meet two times each week for one hour and fifteen minutes each. No unusual audiovisual or technological aids will be used. This course serves as an introduction to game theory as the study of incentives and strategic behavior in collective and interdependent decision making. The course will develop the necessary theoretical tools for the study of game theory, while concurrently introducing applications in areas such as bargaining, competition, auction theory and strategic voting. This is a course indicated for any student with interest in learning how to apply game theoretical analysis to a variety of disciplines. The aim of the course is to provide a mostly applied overview of game theoretical concepts and emphasize their use in real world situations. By the end of the course, the student should have developed tools which will allow her/him to formally analyze outcomes in strategic situations. There will be one midterm and one final exam and approximately 8 problem sets for this class. The midterm and final exam scores count for 30%, and 60% respectively, of your course grade. The problem set score will be calculated ignoring your lowest score during the semester and will count for 10% of the final grade.
E-Service Quality , E-Satisfaction and E-Loyalty of Online Shoppers in Business to Consumer Market ; Evidence form Malaysia

The growing usage of internet and online shopping in Malaysia presents a huge prospect in e-commerce market, specifically for B2C segment. As a result, electronic service quality (e-SQ), electronic satisfaction (e-Satisfaction) and electronic loyalty (e-Loyalty) become vital for online retailers to attract and retain online shoppers in this virtual environment. The association between e-SQ, e-Satisfaction and e-Loyalty should be continuously examined to cope with the advancement in information and communication technology, and the changing expectation of online shoppers. However, construct of e-SQ for online retailers in B2C market is still debatable. In this research, E-SERVQUAL was integrated with the other e-SQ scales to measure e-SQ of a prominent online retailer in Malaysia. Specifically, the e-SQ constructs are Efficiency, Privacy and Trust, Fulfilment, Responsiveness, Contact and Website Design. 390 sets of completed and usable questionnaires were gathered using online questionnaire and convenience sampling procedure. The result indicated that the five proposed dimensions of e-SQ constitute e-SQ of online retailer in B2C market. All the dimensions of e-SQ were found to have positive and significant effect on e-Satisfaction of online shoppers. Responsiveness of e-SQ had the strongest impact on e-satisfaction of online shoppers. The shoppers e-Satisfaction was positively and significantly affected their e-Loyalty towards continuous usage of online retailer’s website. Managerial and theoretical implications are discussed based on the results of the study.
Impact of Parent ’ s Occupation and Family Income on Children ’ s Performance

The main purpose of the study is to examine the impact of parent’s occupation and family income on children performance. For this study a survey was conducted in Southern Punjab. The sample of 15oo parents were collected through a questionnaire using probability sampling technique that is Simple Random Sampling. All the analysis has been carried out on SPSS (Statistical Package for the Social Sciences). Chisquare test is applied to test the effect of parent’s occupation and family income on children’s performance. The results of the study specify that parent’soccupation and family incomehave significant impact on children’s performance.Parents play an important role in child development. Parents with good economic status provide better facilities to their children, results in better performance of the children.
A path reuse method for specular surface reflection in Monte Carlo path tracing

Physically based image synthesis aims to render realistic images, which is widely used in many industries, e.g., computer-generated imagery (CGI) in the movie industry, 3D models preview in the architecture and the design industry, and of course, 3D scenes in the computer game industry. In the real world, the information of light is infinite; therefore, sampling is necessary for light contribution estimation. How to deal with global illumination effects plays an important role in whether the rendered image looks real or not. Many sampling methods reveal different features of the scene. Bidirectional path tracing, one of the Monte Carlo path tracing methods, is known for its rapid convergence. However, due to its innate limitation, bidirectional path tracing cannot handle some difficult cases. The most notorious one is specular-diffuse-specular (SDS) paths. In this paper, we introduce an alternative path reuse method based on bidirectional path tracing to deal with the SDS problem during the operation.
Audiopad: A Tag-based Interface for Musical Performance

We present Audiopad, an interface for musical performance that aims to combine the modularity of knob based controllers with the expressive character of multidimensional tracking interfaces. The performer's manipulations of physical pucks on a tabletop control a real-time synthesis process. The pucks are embedded with LC tags that the system tracks in two dimensions with a series of specially shaped antennae. The system projects graphical information on and around the pucks to give the performer sophisticated control over the synthesis process.  The late nineties saw the emergence of a new musical performance paradigm. Sitting behind the glowing LCDs on their laptops, electronic musicians could play their music in front of audiences without bringing a truckload of synthesizers and patch cables. However, the transition to laptop based performance created a rift between the performer and the audience as there was almost no stage presence for an onlooker to latch on to. Furthermore, the performers lost much of the real-time expressive power of traditional analog instruments. Their on-the-fly arrangements relied on inputs from their laptop keyboards and therefore lacked nuance, finesse, and improvisational capabilities.
OpenFlow: A security analysis

Software Defined Networking (SDN) has been proposed as a drastic shift in the networking paradigm, by decoupling network control from the data plane and making the switching infrastructure truly programmable. The key enabler of SDN, OpenFlow, has seen widespread deployment on production networks and its adoption is constantly increasing. Although openness and programmability are primary features of OpenFlow, security is of core importance for real-world deployment. In this work, we perform a security analysis of OpenFlow using STRIDE and attack tree modeling methods, and we evaluate our approach on an emulated network testbed. The evaluation assumes an attacker model with access to the network data plane. Finally, we propose appropriate counter-measures that can potentially mitigate the security issues associated with OpenFlow networks. Our analysis and evaluation approach are not exhaustive, but are intended to be adaptable and extensible to new versions and deployment contexts of OpenFlow.
Panopticon: Reaping the Benefits of Incremental SDN Deployment in Enterprise Networks

The operational challenges posed in enterprise networks present an appealing opportunity for automated orchestration by way of Software-Defined Networking (SDN). The primary challenge to SDN adoption in the enterprise is the deployment problem: How to deploy and operate a network consisting of both legacy and SDN switches, while benefiting from simplified management and enhanced flexibility of SDN. This paper presents the design and implementation of Panopticon, an architecture for operating networks that combine legacy and SDN switches. Panopticon exposes an abstraction of a logical SDN in a partially upgraded legacy network, where SDN benefits can extend over the entire network. We demonstrate the feasibility and evaluate the efficiency of our approach through both testbed experiments with hardware switches and through simulation on real enterprise campus network topologies entailing over 1500 devices. Our results suggest that when as few as 10% of distribution switches support SDN, most of an enterprise network can be operated as a single SDN while meeting key resource constraints.
Computer Algebra for Fingerprint Matching

We show in this paper how some algebraic methods can be used for fingerprint matching. The described technique is able to compute the score of a match also when the template and test fingerprints have been not correctly acquired. In particular, the match is independent of translations, rotations and scaling transformations of the template. The technique is also able to compute a match score when part of the fingerprint image is incorrect or missed. The algorithm is being implemented in CoCoA, a computer algebra system for doing computations in Commutative Algebra.
Micro-rendering for scalable, parallel final gathering

Recent approaches to global illumination for dynamic scenes achieve interactive frame rates by using coarse approximations to geometry, lighting, or both, which limits scene complexity and rendering quality. High-quality global illumination renderings of complex scenes are still limited to methods based on ray tracing. While conceptually simple, these techniques are computationally expensive. We present an efficient and scalable method to compute global illumination solutions at interactive rates for complex and dynamic scenes. Our method is based on parallel final gathering running entirely on the GPU. At each final gathering location we perform micro-rendering: we traverse and rasterize a hierarchical point-based scene representation into an importance-warped micro-buffer, which allows for BRDF importance sampling. The final reflected radiance is computed at each gathering location using the micro-buffers and is then stored in image-space. We can trade quality for speed by reducing the sampling rate of the gathering locations in conjunction with bilateral upsampling. We demonstrate the applicability of our method to interactive global illumination, the simulation of multiple indirect bounces, and to final gathering from photon maps.
CARDINAL: Computer Assisted Authoring of Movie Scripts

We present Cardinal, a tool for computer-assisted authoring of movie scripts. Cardinal provides a means of viewing a script through a variety of perspectives, for interpretation as well as editing. This is made possible by virtue of intelligent automated analysis of natural language scripts and generating different intermediate representations. Cardinal generates 2-D and 3-D visualizations of the scripted narrative and also presents interactions in a timeline-based view. The visualizations empower the scriptwriter to understand their story from a spatial perspective, and the timeline view provides an overview of the interactions in the story. The user study reveals that users of the system demonstrated confidence and comfort using the system.
PRIMAL: PRofIt Maximization Avatar pLacement for mobile edge computing

We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, and can thus lower the end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain the low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations. Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.