nils-herrmann/clean_scirepeval huggingface dataset:
Performance evaluation of multi-target tracking using the OSPA metric

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design and comparison of tracking systems. Recently a consistent metric for performance evaluation of multi-object filters (referred to as OSPA metric) has been proposed. In this paper we describe how the OSPA metric can be adapted to evaluate the performance of multi-target tracking algorithms. The main idea is to introduce the track label error into consideration in order to capture the data association performance, in addition to the existing cardinality and localisation errors. The paper demonstrates the proposed method by assessing and comparing the performance of two particle filters for multi-target tracking.
Prosocial behavior: multilevel perspectives.

Current research on prosocial behavior covers a broad and diverse range of phenomena. We argue that this large research literature can be best organized and understood from a multilevel perspective. We identify three levels of analysis of prosocial behavior: (a) the "meso" level--the study of helper-recipient dyads in the context of a specific situation; (b) the micro level--the study of the origins of prosocial tendencies and the sources of variation in these tendencies; and (c) the macro level--the study of prosocial actions that occur within the context of groups and large organizations. We present research at each level and discuss similarities and differences across levels. Finally, we consider ways in which theory and research at these three levels of analysis might be combined in future intra- and interdisciplinary research on prosocial behavior.
A review of the production of ethanol from softwood

Ethanol produced from various lignocellulosic materials such as wood, agricultural and forest residues has the potential to be a valuable substitute for, or complement to, gasoline. One of the major resources in the Northern hemisphere is softwood. This paper reviews the current status of the technology for ethanol production from softwood, with focus on hemicellulose and cellulose hydrolysis, which is the major problem in the overall process. Other issues of importance, e.g. overall process configurations and process economics are also considered.
1 Unsupervised Feature Learning in Time Series 2 Prediction Using Continuous Deep Belief Network 3

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this 18 paper, focusing on the problem of weak feature learning ability when dealing with continuous data. 19 In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer 20 functions, the contrastive divergence is designed in hidden layer training process to raise 21 convergence speed, an improved dropout strategy is then implemented in unsupervised training to 22 realize features learning by de-cooperating between the units, and then the network is fine-tuned 23 using back propagation algorithm. Besides, hyper-parameters are analysed through stability 24 analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, 25 CATS benchmark and other real world like CO2 and waste water parameters forecasting show that 26 cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than 27 other methods.
Rapid MPPT for Uniformly and Partial Shaded PV System by Using JayaDE Algorithm in Highly Fluctuating Atmospheric Conditions

In photovoltaic (PV) array, the output power and the power–voltage (P–V ) characteristic of PV array are totally dependent on the temperature and solar insolation. Therefore, if these atmospheric parameters fluctuate rapidly, then the maximum power point (MPP) of the P–V curve of PV array also fluctuates very rapidly. This rapid fluctuation of the MPP may be in accordance with the uniform shading of the PV panel or may be in accordance to the partially shaded due to the clouds, tall building, trees, and raindrops. However, in both cases, the MPP tracking (MPPT) is not only a nonlinear problem, this becomes a highly nonlinear problem, which solution is time bounded. Because the highly fluctuating atmospheric conditions change the P–V characteristic after every small time duration. This paper introduces a hybrid of “Jaya” and “differential evolution (DE)” (JayaDE) technique for MPPT in the highly fluctuating atmospheric conditions. This JayaDE algorithm is tested on MATLAB simulator and is verified on a developed hardware of the solar PV system, which consists of a single peak and many multiple peaks in the voltage–power curve. Moreover, the tracking ability is compared with the recent state of the art methods. The satisfactory steady-state and dynamic performances of this new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.
Form digitization in BPO: from outsourcing to crowdsourcing?

This paper describes an ethnographic study of an outsourced business process - the digitization of healthcare forms. The aim of the study was to understand how the work is currently organized, with an eye to uncovering the research challenges which need to be addressed if that work is to be crowdsourced. The findings are organised under four emergent themes: Workplace Ecology, Data Entry Skills and Knowledge, Achieving Targets and Collaborative Working. For each theme a description of how the work is undertaken in the outsourcer's Indian office locations is given, followed by the implications for crowdsourcing that work. This research is a first step in understanding how crowdsourcing might be applied to BPO activities. The paper examines features specific to form digitization - extreme distribution and form decomposition - and lightly touches on the crowdsourcing of BPO work more generally.
Hurdles to herd immunity: Distrust of government and vaccine refusal in the US, 2002-2003.

High rates of nonmedical exemptions (NMEs) from required childhood vaccinations have contributed to outbreaks of vaccine-preventable diseases, such as measles and pertussis. Understanding the parental decision to obtain an NME could help health professionals and public health programs improve vaccination rates in areas with high vaccine refusal. Using a 2002-2003 multi-state survey of parents of school age children (​n=2445), this study found that parental distrust of the government and of healthcare providers is a significant factor related to a number of vaccine-related beliefs and behaviors. The odds that parents who distrust the government have seen a complementary/alternative medicine (CAM) provider were 2.11 times greater than those of parents who trust the government (70.1% vs 52.6%; OR, 2.11; 95% CI, 1.59-2.84; P<0.01). Parents who distrust the government had increased odds of trusting vaccine information from CAM providers compared to trusting parents (57.9% vs 46.3%; OR, 1.53; 95% CI, 1.16-2.01; P<0.01). Parents who distrust the government also had increased odds of distrusting vaccine information acquired at their healthcare providers' offices (12.6% vs 4.7%; OR, 2.64; 95% CI, 1.64-4.24; P<0.01). Distrustful parents had increased odds of thinking government sources of information about vaccines were unreliable, categorizing the CDC, the Food and Drug Administration (FDA), or local and state health departments as poor or very poor sources (distrust government vs trust government: 25.2% vs 11.7%; OR, 2.39; 95% CI, 1.70-3.36; P<0.01; distrust healthcare providers vs trust healthcare providers: 24.4% vs 11.4%; OR, 2.44; 95% CI, 1.75-3.38; P<0.01). These findings indicate that distrustful parent populations may need to be reached through modalities outside of traditional government and healthcare provider communications. Research into new and more effective techniques for delivering pro-vaccine messages is warranted.
A 10-GS/s 4-Bit Single-Core Digital-to-Analog Converter for Cognitive Ultrawidebands

This brief delineates the design and realization of a 10-GS/s 4-bit digital-to-analog converter (DAC) for the cognitive ultrawideband (CUWB), an emerging solution for low interference and efficient spectrum utilization in communication networks. The DAC serves as the data converter for the adaptive waveform transmitter therein, largely to reduce its power dissipation and hardware complexity. For reasons of low power dissipation and low-cost CUWB application, the resolution of the DAC is 4 bits, its realization is in standard 65-nm CMOS, and the architecture is a single core. The binary current-steering DAC includes critical building blocks such as current sources and a novel deglitcher circuit. The current sources are designed for small area with high linearity based on our derived relationship between current-source output resistance and linearity parameters [integral nonlinearity (INL) and spurious-free dynamic range (SFDR)]. The deglitcher design includes high-speed source followers as high-speed low voltage swing buffers to improve the linearity by decreasing the output glitch energy. The DAC embodies an in situ hardware efficient (small integrated-circuit area and reduced input/output pinout) tester that generates 4 × 10-Gb/s test-data pattern to facilitate functional verification. The designed DAC achieves ≤ 0.16-least significant bit INL/differential nonlinearity and > 23-dBc SFDR over the Nyquist bandwidth up to 4.53 GHz, and features the most competitive figures-of-merit of all similar DACs reported to date.
The Anatomy of a Large-Scale Hypertextual Web Search Engine

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at  To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Overview of narrowband IoT in LTE Rel-13

In 3GPP Rel-13, a narrowband system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, supports most LTE functionalities albeit with essential simplifications to reduce device complexity. Further optimizations to increase coverage, reduce overhead and reduce power consumption while increasing capacity have been introduced as well. The design objectives of NB-IoT include low-complexity devices, high coverage, long device battery life, and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. This paper provides an overview of NB-IoT design, including salient features from the physical and higher layers. Illustrative results with respect to performance objectives are also provided. Finally, NB-IoT enhancements in LTE Rel-14 are briefly outlined.
Fear and the human amygdala.

We have previously reported that bilateral amygdala damage in humans compromises the recognition of fear in facial expressions while leaving intact recognition of face identity (Adolphs et al., 1994). The present study aims at examining questions motivated by this finding. We addressed the possibility that unilateral amygdala damage might be sufficient to impair recognition of emotional expressions. We also obtained further data on our subject with bilateral amygdala damage, in order to elucidate possible mechanisms that could account for the impaired recognition of expressions of fear. The results show that bilateral, but not unilateral, damage to the human amygdala impairs the processing of fearful facial expressions. This impairment appears to result from an insensitivity to the intensity of fear expressed by faces. We also confirmed a double dissociation between the recognition of facial expressions of fear, and the recognition of identity of a face: these two processes can be impaired independently, lending support to the idea that they are subserved in part by anatomically separate neural systems. Based on our data, and on what is known about the amygdala's connectivity, we propose that the amygdala is required to link visual representations of facial expressions, on the one hand, with representations that constitute the concept of fear, on the other. Preliminary data suggest the amygdala's role extends to both recognition and recall of fearful facial expressions.
Lower Bounds for Finding Stationary Points of Non-Convex , Smooth High-Dimensional Functions ∗

We establish lower bounds on the complexity of finding -stationary points of smooth, non-convex, high-dimensional functions. For functions with Lipschitz continuous pth derivative, we show that all algorithms—even randomized algorithms observing arbitrarily high-order derivatives—have worst-case iteration count Ω( −(p+1)/p). Our results imply that the O( −2) convergence rate of gradient descent is unimprovable without additional assumptions (e.g. Lipschitz Hessian), and that cubic regularization of Newton’s method and pth order regularization in general are similarly optimal. Additionally, we prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates better than O( −8/5), which is within −1/15 of the recently established Õ( −5/3) rate for accelerated gradient descent.
EVALUATING THE DEVELOPMENTAL IMPACT OF E-GOVERNANCE INITIATIVES : AN EXPLORATORY FRAMEWORK

India is faced with a large number of challenges that are typical of developing countries. The Indian government has for the past 3 decades widely acknowledged that expanded use of ICT in the public sector can offer important benefits such as improved planning and monitoring mechanisms, cost savings through rationalisation, and more effective administration and delivery of certain public services. Today, wide ranges of e-governance projects are being implemented in different parts of the country including projects aimed at reaching areas and people that had traditionally not been connected to the outside world. This paper uses Sen’s notion of capabilities as an evaluative space for e-governance assessment. Such an emphasis demands a change in the way e-governance is assessed. The capabilities framework allows us to focus on the developmental impact of such projects. We base our evaluation on the FRIENDS and AKSHAYA e-governance projects in the South Indian state of Kerala.
Electrostatic separation of metals and plastics from granular industrial wastes

The paper presents the main conclusions of authors' 15-year involvement in theoretical study, laboratory experimentation, equipment development and industry application of electrostatic separation technologies. The roll-type separator with combined corona-electrostatic field is the most advantageous technical solution when the purpose is to isolate conductive particles from nonconductive ones. The theoretical analysis and the laboratory tests of electrostatic separation point out the multitude of factors which influence the efficiency of this equipment: the configuration of the electric field, the size and the shape of the processed particles, the speed and the radius of the rotating roll electrode and the environmental conditions. The optimum electrode arrangement and operating parameters (high-voltage level, roll speed, feed rate) differ from one application to another. In-plant tests are recommended for the fine tuning of the electrostatic separation technology.
DCnet: A new data center network architecture

The migration of computation and storage to cloud data centers has imposed new communication requirements and a need for fundamental changes in infrastructure. The cloud paradigm means that significant network communication will occur between servers in data centers. Furthermore, the migration of virtual machines and containers across physical servers means that in addition to traditional requirements of efficiency, scalability, and easy manageability, future data center networks must also support efficient mobility. To solve the problem, the paper proposes a radical redesign for data center networks, using a new architectural approach that we call DCnet. The DCnet approach changes the addressing and routing schemes at layers 2 and 3 completely. The new addressing mechanism allows an organization to assign addresses that span multiple data centers and permits VM mobility across data centers. Despite radical changes in addressing and routing, the DCnet architecture retains basics from Ethernet and IP, allowing existing hardware building blocks to be reused. The paper uses IPv6 in examples, but the new addressing model and address mobility approach can be applied to IPv4. By assigning each addressable entity a unique ID, and using the ID as the host suffix in an IP address, DCnet allows legacy operating systems and applications to be used without changes. The paper reports a simulation study that uses mininet and SDN to demonstrate the feasibility of the DCnet approach.
Mining StackOverflow to Filter Out Off-Topic IRC Discussion

Internet Relay Chat (IRC) is a commonly used tool by OpenSource developers. Developers use IRC channels to discuss programming related problems, but much of the discussion is irrelevant and off-topic. Essentially if we treat IRC discussions like email messages, and apply spam filtering, we can try to filter out the spam (the off-topic discussions) from the ham (the programming discussions). Yet we need labelled data that unfortunately takes time to curate.
 To avoid costly curration in order to filter out off-topic discussions, we need positive and negative data-sources. Online discussion forums, such as StackOverflow, are very effective for solving programming problems. By engaging in open-data, StackOverflow data becomes a powerful source of labelled text regarding programming. This work shows that we can train classifiers using StackOverflow posts as positive examples of on-topic programming discussion. YouTube video comments, notorious for their lack of quality, serve as training set of off-topic discussion. By exploiting these datasets, accurate classifiers can be built, tested and evaluated that require very little effort for end-users to deploy and exploit.
Enhanced Eulerian video magnification

A post-processing technique is introduced to improve the Eulerian video magnification method, which is a state-of-the-art motion magnification method to manipulate small movements in videos based on spatio-temporal filtering. The proposed method use the Eulerian video magnification as a video spatio-temporal motion analyzer to get the pixel-level motion mapping. Then the input video pixels are warped based-on this mapping to amplify the motion. This processing does not involve pixel value modifying, which makes it supports larger amplification and is significantly less influenced by the frame noise.
Extract highlights from baseball game video with hidden Markov models

In this paper, we describe a statistical method to detect highlights in a baseball game video. The input video is first segmented into scene shots, within which the camera motion is continuous. Our approach is based on the observations that 1) most highlights in baseball games are composed of certain types of scene shots and 2) those scene shots exhibit special transition context in time. To exploit those two observations, we first build statistical models for each type of scene shots with products of histograms, and then for each type of highlight a hidden Markov model is learned to represent the context of transition in time domain. A probabilistic model can be obtained by combining the two, which is used for highlight detection and classification. Satisfactory results have been achieved on initial experimental results.
GUIDELINES FOR DEPICTING EMOTIONS IN STORYBOARD SCENARIOS

Designers use storyboards to present scenarios of future product use. Despite the benefits of conveying emotional experiences in storyboard scenarios, the design community lacks guidance on how emotion should be depicted in storyboards. We conducted interviews with cartoon artists and used crowdsourcing to classify a large set of emotion examples from comics and manga. Results show that people can recognize instances of happy, angry, and sad emotions rather reliably, but they often misinterpret emotional expressions for fearful, surprised, and disgusted. We extract the most salient features for expressing basic emotions, and create design recommendations for how to depict emotion successfully in storyboards.
Understanding Human Mobility from Twitter

Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short- and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers' movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.
Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving.