nils-herrmann/clean_scirepeval huggingface dataset:
Lipid productivity as a key characteristic for choosing algal species for biodiesel production

Microalgae are a promising alternative source of lipid for biodiesel production. One of the most important decisions is the choice of species to use. High lipid productivity is a key desirable characteristic of a species for biodiesel production. This paper reviews information available in the literature on microalgal growth rates, lipid content and lipid productivities for 55 species of microalgae, including 17 Chlorophyta, 11 Bacillariophyta and five Cyanobacteria as well as other taxa. The data available in the literature are far from complete and rigorous comparison across experiments carried out under different conditions is not possible. However, the collated information provides a framework for decision-making and a starting point for further investigation of species selection. Shortcomings in the current dataset are highlighted. The importance of lipid productivity as a selection parameter over lipid content and growth rate individually is demonstrated.
Emotion in criminal offenders with psychopathy and borderline personality disorder.

Criminal offenders with a diagnosis of psychopathy or borderline personality disorder (BPD) share an impulsive nature but tend to differ in their style of emotional response. This study aims to use multiple psychophysiologic measures to compare emotional responses to unpleasant and pleasant stimuli.



Twenty-five psychopaths as defined by the Hare Psychopathy Checklist and 18 subjects with BPD from 2 high-security forensic treatment facilities were included in the study along with 24 control subjects. Electrodermal response was used as an indicator of emotional arousal, modulation of the startle reflex as a measure of valence, and electromyographic activity of the corrugator muscle as an index of emotional expression.



Compared with controls, psychopaths were characterized by decreased electrodermal responsiveness, less facial expression, and the absence of affective startle modulation. A higher percentage of psychopaths showed no startle reflex. Subjects with BPD showed a response pattern very similar to that of controls, ie, they showed comparable autonomic arousal, and their startle responses were strongest to unpleasant slides and weakest to pleasant slides. However, corrugator electromyographic activity in subjects with BPD demonstrated little facial modulation when they viewed either pleasant or unpleasant slides.



The results support the theory that psychopaths are characterized by a pronounced lack of fear in response to aversive events. Furthermore, the results suggest a general deficit in processing affective information, regardless of whether stimuli are negative or positive. Emotional hyporesponsiveness was specific to psychopaths, since results for offenders with BPD indicate a widely adequate processing of emotional stimuli.
An efficient flow-based botnet detection using supervised machine learning

Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs to be observed per flow in order to capture the patterns of malicious traffic. The proposed system has been tested through the series of experiments using traffic traces originating from two well-known P2P botnets and diverse non-malicious applications. The results of experiments indicate that the system is able to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates a strong potential of using the proposed approach within a future on-line detection framework.
Model of photovoltaic cell circuits under partial shading

The PV-cell and natural energy production systems have been much attracted. Together with global warning and extensive applications of solar power electric generation, defects on PV-cell under the partial shading become interesting technical issue. This paper investigates the equivalent circuit model and its characteristics under the partial shading condition. And we have clarified the mechanism of PV-cell under the partial shading. As the extension of these considerations, we have proposed the new MPPT method (maximal power point tracking) for the sake of drastic energy generation improvement
Debugging Optimized Code with Dynamic Deoptimization

SELF's debugging system provides complete source-level debugging (expected behavior) with globally optimized code. It shields the debugger from optimizations performed by the compiler by dynamically deoptimizing code on demand. Deoptimization only affects the procedure activations that are actively being debugged; all other code runs at full speed. Deoptimization requires the compiler to supply debugging information at discrete interrupt points; the compiler can still perform extensive optimizations between interrupt points without affecting debuggability. At the same time, the inability to interrupt between interrupt points is invisible to the user. Our debugging system also handles programming changes during debugging. Again, the system provides expected behavior: it is possible to change a running program and immediately observe the effects of the change. Dynamic deoptimization transforms old compiled code (which may contain inlined copies of the old version of the changed procedure) into new versions reflecting the current source-level state. To the best of our knowledge, SELF is the first practical system providing full expected behavior with globally optimized code.
Boosting recombined weak classifiers

Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the combination of weak classifiers. Therefore, it is possible to use boosting methods with very simple base classifiers. One of the most simple classifiers are decision stumps, decision trees with only one decision node. This work proposes a variant of the most well-known boosting method, AdaBoost. It is based on considering, as the base classifiers for boosting, not only the last weak classifier, but a classifier formed by the last r selected weak classifiers (r is a parameter of the method). If the weak classifiers are decision stumps, the combination of r weak classifiers is a decision tree. The ensembles obtained with the variant are formed by the same number of decision stumps than the original AdaBoost. Hence, the original version and the variant produce classifiers with very similar sizes and computational complexities (for training and classification). The experimental study shows that the variant is clearly beneficial.
Fast Community Detection for Dynamic Complex Networks

Dynamic complex networks are used to model the evolving relationships between entities in widely varying fields of research such as epidemiology, ecology, sociology, and economics. In the study of complex networks, a network is said to have community structure if it divides naturally into groups of vertices with dense connections within groups and sparser connections between groups. Detecting the evolution of communities within dynamically changing networks is crucial to understanding complex systems. In this paper, we develop a fast community detection algorithm for real-time dynamic network data. Our method takes advantage of community information from previous time steps and thereby improves efficiency while maintaining the quality of community detection. Our experiments on citation-based networks show that the execution time improves as much as 30% (average 13%) over static methods.
End to End Long Short Term Memory Networks for Non-Factoid Question Answering

Retrieving correct answers for non-factoid queries poses significant challenges for current answer retrieval methods. Methods either involve the laborious task of extracting numerous features or are ineffective for longer answers. We approach the task of non-factoid question answering using deep learning methods without the need of feature extraction. Neural networks are capable of learning complex relations based on relatively simple features which make them a prime candidate for relating non-factoid questions to their answers. In this paper, we show that end to end training with a Bidirectional Long Short Term Memory (BLSTM) network with a rank sensitive loss function results in significant performance improvements over previous approaches without the need for combining additional models.
Cost-sensitive learning for large-scale hierarchical classification

We study hierarchical classification of products in electronic commerce, classifying a text description of a product into one of the leaf classes of a tree-structure taxonomy. In particular, we investigate two essential problems, performance evaluation and learning, in a synergistic way. Unless we know what is the appropriate performance evaluation metric for a task, we are not going to learn a classifier of maximum utility for the task. Given the characteristics of the task of hierarchical product classification, we shed insight into how and why common evaluation metrics such as error rate can be misleading, which is applicable for treating other real world applications. The analysis leads to a new performance evaluation metric that tailors this task to reflect a vendor's business goal of maximizing revenue. The proposed metric has an intuitive meaning as the average revenue loss, which depends on both the value of individual products and the hierarchical distance between the true class and the predicted class. Correspondingly, our learning algorithm uses multi-class SVM with margin re-scaling to optimize the proposed metric, instead of error rate or other common metrics. Margin re-scaling is sensitive to the scaling of loss functions. We propose a loss normalization approach to appropriately calibrating the scaling of loss functions, which is applicable to general classification and structured prediction tasks whenever using structured SVM with margin re-scaling. Experiments on a large dataset show that our approach outperforms standard multi-class SVM in terms of the proposed metric, effectively reducing the average revenue loss.
An Online Algorithm for Maximizing Submodular Functions

We consider the following two problems. We are given as input a set of activities and a set of jobs to complete. Our goal is to devise a schedule for allocating time to the various activities so as to achieve one of two objectives: minimizing the average time required to complete each job, or maximizing the number of jobs completed within a fixed time T . Formally, a schedule is a sequence 〈(v1, τ1), (v2, τ2), . . .〉, where each pair (v, τ) represents investing time τ in activity v. We assume that the fraction of jobs completed, f , is a monotone submodular function of the sequence of pairs that appear in a schedule. In the offline setting in which we have oracle access to f , these two objectives give us, respectively, what we call the MIN SUM SUBMODULAR COVER problem (which is a generalization of the MIN SUM SET COVER problem and the related PIPELINED SET COVER problem) and what we call BUDGETED MAXIMUM SUBMODULAR COVERAGE (which generalizes the problem of maximizing a monotone, submodular function subject to a knapsack constraint). We consider these problems in the online setting, in which the jobs arrive one at a time and we must finish each job (via some schedule) before moving on to the next. We give an efficient online algorithm for this problem whose worst-case asymptotic performance is simultaneously optimal for both objectives (unless P = NP), in the sense that its performance ratio (with respect to the optimal static schedule) converges to the best approximation ratios for the corresponding offline problems. Finally, we evaluate this algorithm experimentally by using it to learn, online, a schedule for allocating CPU time to the solvers entered in the 2007 SAT solver competition.
Nicky: Toward a Virtual Assistant for Test and Measurement Instrument Recommendations

Natural language question answering has been an area of active computer science research for decades. Recent advances have led to a new generation of virtual assistants or chatbots, frequently based on semantic modeling of some broadly general domain knowledge. However, answering questions about detailed, highly technical, domain-specific capabilities and attributes remains a difficult and complex problem. In this paper we discuss a prototype conversational virtual assistant designed for choosing test and measurement equipment based on the detailed measurement requirements of the test engineer. Our system allows for multi-stage queries which retain sufficient short-term context to support query refinement as well as compound questions. In addition to the software architecture, we explore an approach to ontology development that leverages inference from reasoners and minimizes the complexity of entering the specifications for a large collection of instruments. Finally, we provide insights into the issues of building this system and provide recommendations for future designs.
Semi-Supervised Web Wrapper Repair via Recursive Tree Matching

Continuous data extraction pipelines using wrappers have become common and integral parts of businesses dealing with stock, flight, or product information. Extracting data from websites that use HTML templates is difficult because available wrapper methods are not designed to deal with websites that change over time (the inclusion or removal of HTML elements). We are the first to perform large scale empirical analyses of the causes of shift and propose the concept of domain entropy to quantify it. We draw from this analysis to propose a new semi-supervised search approach called XTPath. XTPath combines the existing XPath with carefully designed annotation extraction and informed search strategies. XTPath is the first method to store contextual node information from the training DOM and utilize it in a supervised manner. We utilize this data with our proposed recursive tree matching method which locates nodes most similar in context. The search is based on a heuristic function that takes into account the similarity of a tree compared to the structure that was present in the training data. We systematically evaluate XTPath using 117,422 pages from 75 diverse websites in 8 vertical markets that covers vastly different topics. Our XTPath method consistently outperforms XPath and a current commercial system in terms of successful extractions in a blackbox test. We are the first supervised wrapper extraction method to make our code and datasets available (online here: http://kdl.cs.umb.edu/w/datasets/).
Question Answering from Frequently Asked Question Files: Experiences with the FAQ FINDER System

guage question-answering system that uses files of frequently asked questions as its knowledge base. Unlike AI question-answering systems that focus on the generation of new answers, FAQ FINDER retrieves existing ones found in frequently asked question files. Unlike information-retrieval approaches that rely on a purely lexical metric of similarity between query and document, FAQ FINDER uses a semantic knowledge base (WORDNET) to improve its ability to match question and answer. We include results from an evaluation of the system’s performance and show that a combination of semantic and statistical techniques works better than any single approach.
METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments

We describe METEOR, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machineproduced translation and human-produced reference translations. Unigrams can be matched based on their surface forms, stemmed forms, and meanings; furthermore, METEOR can be easily extended to include more advanced matching strategies. Once all generalized unigram matches between the two strings have been found, METEOR computes a score for this matching using a combination of unigram-precision, unigram-recall, and a measure of fragmentation that is designed to directly capture how well-ordered the matched words in the machine translation are in relation to the reference. We evaluate METEOR by measuring the correlation between the metric scores and human judgments of translation quality. We compute the Pearson R correlation value between its scores and human quality assessments of the LDC TIDES 2003 Arabic-to-English and Chinese-to-English datasets. We perform segment-bysegment correlation, and show that METEOR gets an R correlation value of 0.347 on the Arabic data and 0.331 on the Chinese data. This is shown to be an improvement on using simply unigramprecision, unigram-recall and their harmonic F1 combination. We also perform experiments to show the relative contributions of the various mapping modules.
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
SPECTRAL BASIS NEURAL NETWORKS FOR REAL-TIME TRAVEL TIME FORECASTING

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and realtime profile. It was found that the SNN gave the best overall results.
Efficient Ranking from Pairwise Comparisons

The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking, biological applications and others. In many practical situations the true pairwise comparisons cannot be actively measured, but a subset of all n(n−1)/2 comparisons is passively and noisily observed. Optimization algorithms (e.g., the SVM) could be used to predict a ranking with fixed expected Kendall tau distance, while achieving an Ω(n) lower bound on the corresponding sample complexity. However, due to their centralized structure they are difficult to extend to online or distributed settings. In this paper we show that much simpler algorithms can match the same Ω(n) lower bound in expectation. Furthermore, if an average of O(n log(n)) binary comparisons are measured, then one algorithm recovers the true ranking in a uniform sense, while the other predicts the ranking more accurately near the top than the bottom. We discuss extensions to online and distributed ranking, with benefits over traditional alternatives.
Low power link layer security for IoT: Implementation and performance analysis

In this paper, we present the implementation and performance evaluation of security functionalities at the link layer of IEEE 802.15.4-compliant IoT devices. Specifically, we implement the required encryption and authentication mechanisms entirely in software and as well exploit the hardware ciphers that are made available by our IoT platform. Moreover, we present quantitative results on the memory footprint, the execution time and the energy consumption of selected implementation modes and discuss some relevant tradeoffs. As expected, we find that hardware-based implementations are not only much faster, leading to latencies shorter than two orders of magnitude compared to software-based security suites, but also provide substantial savings in terms of ROM memory occupation, i.e. up to six times, and energy consumption. Furthermore, the addition of hardware-based security support at the link layer only marginally impacts the network lifetime metric, leading to worst-case reductions of just 2% compared to the case where no security is employed. This is due to the fact that energy consumption is dominated by other factors, including the transmission and reception of data packets and the control traffic that is required to maintain the network structures for routing and data collection. On the other hand, entirely software-based implementations are to be avoided as the network lifetime reduction in this case can be as high as 25%.