nils-herrmann/clean_scirepeval huggingface dataset:
Long Range Audio and Audio-Visual Event Detection Using a Laser Doppler Vibrometer

Association of audio events with video events presents a challenge to a typical camera-microphone approach in order to capture AV signals from a large distance. Setting up a long range microphone array and performing geo-calibration of both audio and video sensors is difficult. In this work, in addition to a geo-calibrated electro-optical camera, we propose to use a novel optical sensor a Laser Doppler Vibrometer (LDV) for real-time audio sensing, which allows us to capture acoustic signals from a large distance, and to use the same geo-calibration for both the camera and the audio (via LDV). We have promising preliminary results on association of the audio recording of speech with the video of the human speaker.
Analysis of infusion pump error logs and their significance for health care.

Infusion therapy is one of the largest practised therapies in any healthcare organisation, and infusion pumps are used to deliver millions of infusions every year in the NHS. The aircraft industry downloads information from 'black boxes' to help design better systems and reduce risk; however, the same cannot be said about error logs and data logs from infusion pumps. This study downloaded and analysed approximately 360 000 hours of infusion pump error logs from 131 infusion pumps used for up to 2 years in one large acute hospital. Staff had to manage 260 129 alarms; this accounted for approximately 5% of total infusion time, costing about £1000 per pump per year. This paper describes many such insights, including numerous technical errors, propensity for certain alarms in clinical conditions, logistical issues and how infrastructure problems can lead to an increase in alarm conditions. Routine use of error log analysis, combined with appropriate management of pumps to help identify improved device design, use and application is recommended.
Studies on the Production of Some Important Secondary Metabolites from Medicinal Plants by Plant Tissue Cultures

Plants are a tremendous source for the discovery of new products of medicinal value for drug development. Today several distinct chemicals derived from plants are important drugs currently used in one or more countries in the world. Many of the drugs sold today are simple synthetic modifications or copies of the naturally obtained substances. The evolving commercial importance of secondary metabolites has in recent years resulted in a great interest in secondary metabolism, particularly in the possibility of altering the production of bioactive plant metabolites by means of tissue culture technology. Plant cell culture technologies were introduced at the end of the 1960’s as a possible tool for both studying and producing plant secondary metabolites. Different strategies, using an in vitro system, have been extensively studied to improve the production of plant chemicals. The focus of the present review is the application of tissue culture technology for the production of some important plant pharmaceuticals. Also, we describe the results of in vitro cultures and production of some important secondary metabolites obtained in our laboratory.
Passion in sport: on the quality of the coach-athlete relationship.

Vallerand et al. (2003) developed a dualistic model of passion, wherein two types of passion are proposed: harmonious (HP) and obsessive (OP) passion that predict adaptive and less adaptive interpersonal outcomes, respectively. In the present research, we were interested in understanding the role of passion in the quality of coach-athlete relationships. Results of Study 1, conducted with athletes (N=157), revealed that HP positively predicts a high-quality coach-athlete relationship, whereas OP was largely unrelated to such relationships. Study 2 was conducted with coaches (N=106) and showed that only HP positively predicted the quality of the coach-athlete relationship. Furthermore, these effects were fully mediated by positive emotions. Finally, the quality of the coach-athlete relationship positively predicted coaches' subjective well-being. Future research directions are discussed in light of the dualistic model of passion.
COMET: Code Offload by Migrating Execution Transparently

In this paper we introduce a runtime system to allow unmodified multi-threaded applications to use multiple machines. The system allows threads to migrate freely between machines depending on the workload. Our prototype, COMET (Code Offload by Migrating Execution Transparently), is a realization of this design built on top of the Dalvik Virtual Machine. COMET leverages the underlying memory model of our runtime to implement distributed shared memory (DSM) with as few interactions between machines as possible. Making use of a new VM-synchronization primitive, COMET imposes little restriction on when migration can occur. Additionally, enough information is maintained so one machine may resume computation after a network failure. We target our efforts towards augmenting smartphones or tablets with machines available in the network. We demonstrate the effectiveness of COMET on several real applications available on Google Play. These applications include image editors, turn-based games, a trip planner, and math tools. Utilizing a server-class machine, COMET can offer significant speed-ups on these real applications when run on a modern smartphone. With WiFi and 3G networks, we observe geometric mean speed-ups of 2.88X and 1.27X relative to the Dalvik interpreter across the set of applications with speed-ups as high as 15X on some applications.
Comparing holistic and atomistic evaluation of evidence

Fact finders in legal trials often need to evaluate a mass of weak, contradictory and ambiguous evidence. There are two general ways to accomplish this task: by holistically forming a coherent mental representation of the case, or by atomistically assessing the probative value of each item of evidence and integrating the values according to an algorithm. Parallel constraint satisfaction (PCS) models of cognitive coherence posit that a coherent mental representation is created by discounting contradicting evidence, inflating supporting evidence and interpreting ambivalent evidence in a way coherent with the emerging decision. This leads to inflated support for whichever hypothesis the fact finder accepts as true. Using a Bayesian network to model the direct dependencies between the evidence, the intermediate hypotheses and the main hypothesis, parameterised with (conditional) subjective probabilities elicited from the subjects, I demonstrate experimentally how an atomistic evaluation of evidence leads to a convergence of the computed posterior degrees of belief in the guilt of the defendant of those who convict and those who acquit. The atomistic evaluation preserves the inherent uncertainty that largely disappears in a holistic evaluation. Since the fact finders’ posterior degree of belief in the guilt of the defendant is the relevant standard of proof in many legal systems, this result implies that using an atomistic evaluation of evidence, the threshold level of posterior belief in guilt required for a conviction may often not be reached. ⃰ Max Planck Institute for Research on Collective Goods, Bonn
Feed Forward and Backward Run in Deep Convolution Neural Network

Convolution Neural Networks (CNN), known as ConvNets are widely used in many visual imagery application, object classification, speech recognition. After the implementation and demonstration of the deep convolution neural network in Imagenet classification in 2012 by krizhevsky, the architecture of deep Convolution Neural Network is attracted many researchers. This has led to the major development in Deep learning frameworks such as Tensorflow, caffe, keras, theno. Though the implementation of deep learning is quite possible by employing deep learning frameworks, mathematical theory and concepts are harder to understand for new learners and practitioners. This article is intended to provide an overview of ConvNets architecture and to explain the mathematical theory behind it including activation function, loss function, feedforward and backward propagation. In this article, grey scale image is taken as input information image, ReLU and Sigmoid activation function are considered for developing the architecture and cross-entropy loss function is used for computing the difference between predicted value and actual value. The architecture is developed in such a way that it can contain one convolution layer, one pooling layer, and multiple dense layers.
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning

Dealing with high-dimensional input spaces, like visual input, is a challenging task for reinforcement learning (RL). Neuroevolution (NE), used for continuous RL problems, has to either reduce the problem dimensionality by (1) compressing the representation of the neural network controllers or (2) employing a pre-processor (compressor) that transforms the high-dimensional raw inputs into low-dimensional features. In this paper, we are able to evolve extremely small recurrent neural network (RNN) controllers for a task that previously required networks with over a million weights. The high-dimensional visual input, which the controller would normally receive, is first transformed into a compact feature vector through a deep, max-pooling convolutional neural network (MPCNN). Both the MPCNN preprocessor and the RNN controller are evolved successfully to control a car in the TORCS racing simulator using only visual input. This is the first use of deep learning in the context evolutionary RL.
Novel Cellular Active Array Antenna System at Base Station for Beyond 4G

This paper introduces a base station antenna system as a future cellular technology. The base station antenna system is the key to achieving high-speed data transmission. It is particularly important to improve the frequency reuse factor as one of the roles of a base station. Furthermore, in order to solve the interference problem due to the same frequency being used by the macro cell and the small cell, the author focuses on beam and null control using an AAS (Active Antenna System) and elucidates their effects through area simulations and field tests. The results showed that AAS can improve the SINR (signal to interference-plusnoise ratio) of the small cell area inside macro cells. The paper shows that cell quality performance can be improved by incorporating the AAS into a cellular base station as its antenna system for beyond 4G radio access technology including the 5G cellular system. key words: active antenna system, null, cellular, base station antenna, small cell
Big Data Privacy in the Internet of Things Era

Over the last few years, we've seen a plethora of Internet of Things (IoT) solutions, products, and services make their way into the industry's marketplace. All such solutions will capture large amounts of data pertaining to the environment as well as their users. The IoT's objective is to learn more and better serve system users. Some IoT solutions might store data locally on devices ("things"), whereas others might store it in the cloud. The real value of collecting data comes through data processing and aggregation on a large scale, where new knowledge can be extracted. However, such procedures can lead to user privacy issues. This article discusses some of the main challenges of privacy in the IoT as well as opportunities for research and innovation. The authors also introduce some of the ongoing research efforts that address IoT privacy issues.
Model transformation by example using inductive logic programming

Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming (Muggleton and Raedt in J Logic Program 19-20:629–679, 1994) which aims at the inductive construction of first-order clausal theories from examples and background knowledge.
Improved Successive Cancellation Decoding of Polar Codes

As improved versions of the successive cancellation (SC) decoding algorithm, the successive cancellation list (SCL) decoding and the successive cancellation stack (SCS) decoding are used to improve the finite-length performance of polar codes. In this paper, unified descriptions of the SC, SCL, and SCS decoding algorithms are given as path search procedures on the code tree of polar codes. Combining the principles of SCL and SCS, a new decoding algorithm called the successive cancellation hybrid (SCH) is proposed. This proposed algorithm can provide a flexible configuration when the time and space complexities are limited. Furthermore, a pruning technique is also proposed to lower the complexity by reducing unnecessary path searching operations. Performance and complexity analysis based on simulations shows that under proper configurations, all the three improved successive cancellation (ISC) decoding algorithms can approach the performance of the maximum likelihood (ML) decoding but with acceptable complexity. With the help of the proposed pruning technique, the time and space complexities of ISC decoders can be significantly reduced and be made very close to those of the SC decoder in the high signal-to-noise ratio regime.
Nonlinearity correction algorithm for wideband FMCW radars

This paper presents a novel nonlinearity correction algorithm for wideband frequency modulated continuous wave (FMCW) radars based on high-order ambiguity functions (HAF) and time resampling. By emphasizing the polynomial-phase nature of the FMCW signal, it is shown that the HAF is an excellent tool for estimating the sweep nonlinearity polynomial coefficients. The estimated coefficients are used to build a correction function which is applied to the beat signal by time resampling. The nonlinearity correction algorithm is tested by simulation and validated on real data sets acquired with an X-band FMCW radar.
Interferences from blood collection tube components on clinical chemistry assays

Improper design or use of blood collection devices can adversely affect the accuracy of laboratory test results. Vascular access devices, such as catheters and needles, exert shear forces during blood flow, which creates a predisposition to cell lysis. Components from blood collection tubes, such as stoppers, lubricants, surfactants, and separator gels, can leach into specimens and/or adsorb analytes from a specimen; special tube additives may also alter analyte stability. Because of these interactions with blood specimens, blood collection devices are a potential source of pre-analytical error in laboratory testing. Accurate laboratory testing requires an understanding of the complex interactions between collection devices and blood specimens. Manufacturers, vendors, and clinical laboratorians must consider the pre-analytical challenges in laboratory testing. Although other authors have described the effects of endogenous substances on clinical assay results, the effects/impact of blood collection tube additives and components have not been well systematically described or explained. This review aims to identify and describe blood collection tube additives and their components and the strategies used to minimize their effects on clinical chemistry assays.
The human NAD metabolome: Functions, metabolism and compartmentalization

The metabolism of NAD has emerged as a key regulator of cellular and organismal homeostasis. Being a major component of both bioenergetic and signaling pathways, the molecule is ideally suited to regulate metabolism and major cellular events. In humans, NAD is synthesized from vitamin B3 precursors, most prominently from nicotinamide, which is the degradation product of all NAD-dependent signaling reactions. The scope of NAD-mediated regulatory processes is wide including enzyme regulation, control of gene expression and health span, DNA repair, cell cycle regulation and calcium signaling. In these processes, nicotinamide is cleaved from NAD(+) and the remaining ADP-ribosyl moiety used to modify proteins (deacetylation by sirtuins or ADP-ribosylation) or to generate calcium-mobilizing agents such as cyclic ADP-ribose. This review will also emphasize the role of the intermediates in the NAD metabolome, their intra- and extra-cellular conversions and potential contributions to subcellular compartmentalization of NAD pools.
Are you sleeping?: sharing portrayed sleeping status within a social network

Within a group of peers, it is often useful or interesting to know whether someone in the group has gone to bed or whether they have awakened in the morning. This information, naturally integrated as a peripheral augmentation of an alarm clock, allows people to know whether it is appropriate to make a call or feel more connected with someone living remotely. In this paper, we present the design and evaluation of such an alarm clock, the BuddyClock, and describe how it enables users in a small social network to automatically share information about their sleeping behaviors with one another. Through 3-6 week deployment studies of this technology with five different social networks, we found that the alarm clock affected participant behaviors and allowed them to feel more connected to those with whom they shared their sleeping behaviors.
Artificial Intelligence learning based on proportional navigation guidance

Artificial Intelligence (AI) is a vast domain with variety of applications. This research proposes the use of AI for guidance of an Unmanned Aerial Vehicle (UAV) to a maneuvering target. The AI agent is trained using machine learning algorithm. The PN guidance algorithm is used as a basis for training the agent. An orthogonal array based training strategy is proposed that provides a better training and reduced miss distance values. The AI based guidance provides performance equal to the PN Guidance law and in some cases it outperforms PN guidance law.
Charge-Trapping Phenomena in HfO2-Based FeFET-Type Nonvolatile Memories

Ferroelectric field effect transistors (FeFETs) based on ferroelectric hafnium oxide (HfO2) thin films show high potential for future embedded nonvolatile memory applications. However, HfO2 films besides their recently discovered ferroelectric behavior are also prone to undesired charge trapping effects. Therefore, the scope of this paper is to verify the possibility of the charge trapping during standard operation of the HfO2-based FeFET memories. The kinetics of the charge trapping and its interplay with the ferroelectric polarization switching are analyzed in detail using the single-pulse ID-VG technique. Furthermore, the impact of the charge trapping on the important memory characteristics such as retention and endurance is investigated.
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration

How many labeled examples are needed to estimate a classifier's performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by making a few reasonable assumptions on the structure of the data, it is possible to estimate performance curves, with confidence bounds, using a small number of ground truth labels. Our approach, which we call Semi supervised Performance Evaluation (SPE), is based on a generative model for the classifier's confidence scores. In addition to estimating the performance of classifiers on new datasets, SPE can be used to recalibrate a classifier by re-estimating the class-conditional confidence distributions.
ExtraSensory App: Data Collection In-the-Wild with Rich User Interface to Self-Report Behavior

We introduce a mobile app for collecting in-the-wild data, including sensor measurements and self-reported labels describing people's behavioral context (e.g., driving, eating, in class, shower). Labeled data is necessary for developing context-recognition systems that serve health monitoring, aging care, and more. Acquiring labels without observers is challenging and previous solutions compromised ecological validity, range of behaviors, or amount of data. Our user interface combines past and near-future self-reporting of combinations of relevant context-labels. We deployed the app on the personal smartphones of 60 users and analyzed quantitative data collected in-the-wild and qualitative user-experience reports. The interface's flexibility was important to gain frequent, detailed labels, support diverse behavioral situations, and engage different users: most preferred reporting their past behavior through a daily journal, but some preferred reporting what they're about to do. We integrated insights from this work back into the app, which we make available to researchers for conducting in-the-wild studies.
Soybean Productivity Modelling using Decision Tree Algorithms

Data mining applications in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. In the present study an attempt has been made to study the influence of climatic parameters on soybean productivity using decision tree induction technique. The findings of Decision tree were framed into different rules for better understanding by the end users. The study findings will help the researchers, policy makers and farmers in predicting/forecasting the crop yield in advance for market dynamics.  Decision tree, crop productivity, ID3 algorithm, climatic factors
Bike-Share Systems : Accessibility and Availability

The cities of Paris, London, Chicago, and New York (among others) have recently launched large-scale bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the relationship between aspects of bike-share system design and ridership. Specifically, we estimate the effects on ridership of station accessibility (how far the commuter must walk to reach a station) and of bike-availability (the likelihood of finding a bike at the station). Our analysis is based on a structural demand model that considers the random-utility maximizing choices of spatially distributed commuters, and it is estimated using highfrequency system-use data from the bike-share system in Paris. The role of station accessibility is identified using cross-sectional variation in station location and high -frequency changes in commuter choice sets; bike-availability effects are identified using longitudinal variation. Because the scale of our data, (in particular the high-frequency changes in choice sets) render traditional numerical estimation techniques infeasible, we develop a novel transformation of our estimation problem: from the time domain to the “station stockout state” domain. We find that a 10% reduction in distance traveled to access bike-share stations (about 13 meters) can increase system-use by 6.7% and that a 10% increase in bikeavailability can increase system-use by nearly 12%. Finally, we use our estimates to develop a calibrated counterfactual simulation demonstrating that the bike-share system in central Paris would have 29.41% more ridership if its station network design had incorporated our estimates of commuter preferences—with no additional spending on bikes or docking points.