nils-herrmann/clean_scirepeval huggingface dataset:
Curved Hexagonal Packings of Equal Disks in a Circle

For each k ≥ 1 and corresponding hexagonal number h(k) = 3k(k + 1) + 1, we introduce m(k) = max{ (k−1)! 2 , 1} packings of h(k) equal disks inside a circle which we call the curved hexagonal packings. The curved hexagonal packing of 7 disks (k = 1, m(1) = 1) is well known and the one of 19 disks (k = 2, m(2) = 1) has been previously conjectured to be optimal. New curved hexagonal packings of 37, 61, and 91 disks (k = 3, 4, and 5, m(3) = 1, m(4) = 3, and m(5) = 12) were the densest we obtained on a computer using a so-called “billiards” simulation algorithm. A curved hexagonal packing pattern is invariant under a 60 rotation. For k → ∞, the density (covering fraction) of curved hexagonal packings tends to π 2 12 . The limit is smaller than the density of the known optimum disk packing in the infinite plane. We found disk configurations that are denser than curved hexagonal packings for 127, 169, and 217 disks (k = 6, 7, and 8). In addition to new packings for h(k) disks, we present new packings we found for h(k) + 1 and h(k) − 1 disks for k up to 5, i.e., for 36, 38, 60, 62, 90, and 92 disks. The additional packings show the “tightness” of the curved hexagonal pattern for k ≤ 5: deleting a disk does not change the optimum packing and its quality significantly, but adding a disk causes a substantial rearrangement in the optimum packing and substantially decreases the quality. CURVED HEXAGONAL PACKINGS OF EQUAL DISKS IN A CIRCLE B. D. Lubachevsky R. L. Graham AT&T Bell Laboratories, Murray Hill, New Jersey 07974
SELECTION USING BINARY ARTIFICIAL BEE COLONY FOR SENTIMENT CLASSIFICATION

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Nowadays people are more interested to express and share their views, feedbacks, suggestions, and opinions about a particular topic on the web. People and company rely more on online opinions about products and services for their decision making. A major problem in identifying the opinion classification is high dimensionality of the feature space. Most of these features are irrelevant, redundant, and noisy which affects the performance of the classifier. Therefore, feature selection is an essential step in the fake review detection to reduce the dimensionality of the feature space and to improve accuracy. In this paper, binary artificial bee colony (BABC) with KNN is proposed to solve feature selection problem for sentiment classification. The experimental results demonstrate that the proposed method selects more informative features set compared to the competitive methods as it attains higher classification accuracy.
A Structured Learning Approach to Temporal Relation Extraction

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.
Interventions for the prevention of dry socket: an evidence-based update

This paper reviews the latest evidence for local and systemic interventions for the prevention of alveolar osteitis (dry socket). Dry socket is a painful and common post-operative complication following exodontia. Any interventions for the prevention of dry socket could reduce both its incidence and help avoid this painful complication. Prophylactic measures proposed in the literature are discussed. Furthermore, this article discusses both the clinical and histological stages of a normal healing socket.
Unreeling netflix: Understanding and improving multi-CDN movie delivery

Netflix is the leading provider of on-demand Internet video streaming in the US and Canada, accounting for 29.7% of the peak downstream traffic in US. Understanding the Netflix architecture and its performance can shed light on how to best optimize its design as well as on the design of similar on-demand streaming services. In this paper, we perform a measurement study of Netflix to uncover its architecture and service strategy. We find that Netflix employs a blend of data centers and Content Delivery Networks (CDNs) for content distribution. We also perform active measurements of the three CDNs employed by Netflix to quantify the video delivery bandwidth available to users across the US. Finally, as improvements to Netflix's current CDN assignment strategy, we propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy, and demonstrate their potentials in significantly increasing user's average bandwidth.
Nagging: A scalable fault-tolerant paradigm for distributed search

This paper describes nagging, a technique for parallelizing search in a heterogeneous distributed computing environment. Nagging exploits the speedup anomaly often observed when parallelizing problems by playing multiple reformulations of the problem or portions of the problem against each other. Nagging is both fault tolerant and robust to long message latencies. In this paper, we show how nagging can be used to parallelize several different algorithms drawn from the artificial intelligence literature, and describe how nagging can be combined with partitioning, the more traditional search parallelization strategy. We present a theoretical analysis of the advantage of nagging with respect to partitioning, and give empirical results obtained on a cluster of 64 processors that demonstrate nagging’s effectiveness and scalability as applied to A* search, α β minimax game tree search, and the Davis-Putnam algorithm.
Business Process Modeling for Domain Inbound Logistics System : Analytic Perspective with BPMN 2 . 0

Among different Business Process Management strategies and methodologies, one common feature is to capture existing processes and representing the new processes adequately. Business Process Modelling (BPM) plays a crucial role on such an effort. This paper proposes a “to-be” inbound logistics business processes model using BPMN 2.0 standard specifying the structure and behaviour of the system within the SME environment. The generic framework of inbound logistics model consists of one main high-level module-based system named Order System comprising of four main sub-systems of the Order core, Procure, Auction, and Purchase systems. The system modelingis elaborately discussed to provide a business analytical perspective from various activities in inbound logistics system. Since the main purpose of the paper is to map out the functionality and behaviour of Logistics system requirements, employing the model is of a great necessity on the future applications at system development such as in the data modelling effort. Moreover, employing BPMN 2.0 method and providing explanatory techniques as a nifty guideline and framework to assist the business process practitioners, analysts and managers at identical systems.
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods. Convolutional neural network (CNN), adaptive deep pyramid matching (ADPM), convolutional features, multi-scale ensemble, remote-sensing scene classification.
SOFSEM 2018: Theory and Practice of Computer Science

Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine-learning and analytics packages. We present KRR formalisms and a system achieving these goals. To this aim, we use specific suitable fragments from the Datalog± family of languages, and we introduce the vadalog system, which puts these swift logics into action. This system exploits the theoretical underpinning of relevant Datalog± languages and combines it with existing and novel techniques from database and AI practice.
To treat or not to treat: puberty suppression in childhood-onset gender dysphoria

Puberty suppression using gonadotropin-releasing-hormone analogues (GnRHa) has become increasingly accepted as an intervention during the early stages of puberty (Tanner stage 2–3) in individuals with clear signs of childhood-onset gender dysphoria. However, lowering the age threshold for using medical intervention for children with gender dysphoria is still a matter of contention, and is more controversial than treating the condition in adolescents and adults, as children with gender dysphoria are more likely to express an unstable pattern of gender variance. Furthermore, concerns have been expressed regarding the risks of puberty suppression, which are poorly understood, and the child's ability to make decisions and provide informed consent. However, even if the limited data available mean that it is not possible to make a conclusive treatment recommendation, some safety criteria for puberty suppression can be identified and applied.
Explaining prediction models and individual predictions with feature contributions

We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method’s usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method’s explanations improved the participants’ understanding of the model.
An Experimental Test of the Impact of Overconfidence and Gender on Trading Activity

We perform an asset market experiment in order to investigate whether overconfidence induces trading. We investigate three manifestations of overconfidence: calibration-based overconfidence, the better-than-average effect and illusion of control. Novelly, the measure employed for calibration-based overconfidence is task-specific in that it is designed to influence behavior. We find that calibration-based overconfidence does engender additional trade, though the better-than-average also appears to play a role. This is true both at the level of the individual and also at the level of the market. There is little evidence that gender influences trading activity.  The authors gratefully acknowledge the co-editor’s valuable suggestions in improving the paper’s exposition and two anonymous referrers’ valuable comments. In addition, the authors would like to thank the very helpful comments of Lucy Ackert, Ben Amoako-Adu, Bruno Biais, Tim Cason, Narat Charupat, Günter Franke, Simon Gervais, Markus Glaser, Patrik Guggenberger, Michael Haigh, Joachim Inkmann, Marhuenda Joaquin, Alexander Kempf, Brian Kluger, Roman Kraeussl, Bina Lehmann, Tao Lin, Harald Lohre, Greg Lypny, Elizabeth Maynes, Moshe Milevsky, Dean Mountain, Gordon Roberts, Chris Robinson, Stefan Rünzi, Gideon Saar, Dirk Schiereck, Harris Schlesinger, Chuck Schnitzlein, Michael Schröder, Betty Simkins, Brian Smith, Issouf Soumare, Yisong Tian, Chris Veld, Boyce Watkins, Martin Weber and Stephan Wiehler, along with seminar participants from American Finance Association 2005 (Philadelphia), the Economic Science Association 2004 (Amsterdam), the Financial Management Association 2004 (New Orleans), the Financial Management Association European Meeting 2004 (Zurich), European Financial Management Association 2004 (Basle), the Northern Finance Association (St. John’s, Newfoundland), the 2004 Symposium for Experimental Finance at the Aston Centre for Experimental Finance (Aston Business School), the 2005 Federal Reserve Bank of Atlanta Experimental Finance Conference, the University of Köln, the University of Konstanz, McMaster University, the University of Tilburg, Wilfred Laurier University and York University. Valuable technical assistance was provided by Harald Lohre, Amer Mohamed and John O’Brien. Generous financial assistance from ZEW, Institut de Finance Mathématiques de Montréal and SSHRC is gratefully acknowledged. Any views expressed represent those of the authors only and not necessarily those of McKinsey & Company, Inc. C
The journey to autism: Insights from neuroimaging studies of infants and toddlers.

By definition, autism spectrum disorder (ASD) is a neurodevelopmental disorder that emerges during early childhood. It is during this time that infants and toddlers transition from appearing typical across multiple domains to exhibiting the behavioral phenotype of ASD. Neuroimaging studies focused on this period of development have provided crucial knowledge pertaining to this process, including possible mechanisms underlying pathogenesis of the disorder and offering the possibility of prodromal or presymptomatic prediction of risk. In this paper, we review findings from structural and functional brain imaging studies of ASD focused on the first years of life and discuss implications for next steps in research and clinical applications.
The effects of high-intensity interval training on glucose regulation and insulin resistance: a meta-analysis.

The aim of this meta-analysis was to quantify the effects of high-intensity interval training (HIIT) on markers of glucose regulation and insulin resistance compared with control conditions (CON) or continuous training (CT). Databases were searched for HIIT interventions based upon the inclusion criteria: training ≥2 weeks, adult participants and outcome measurements that included insulin resistance, fasting glucose, HbA1c or fasting insulin. Dual interventions and participants with type 1 diabetes were excluded. Fifty studies were included. There was a reduction in insulin resistance following HIIT compared with both CON and CT (HIIT vs. CON: standardized mean difference [SMD] = -0.49, confidence intervals [CIs] -0.87 to -0.12, P = 0.009; CT: SMD = -0.35, -0.68 to -0.02, P = 0.036). Compared with CON, HbA1c decreased by 0.19% (-0.36 to -0.03, P = 0.021) and body weight decreased by 1.3 kg (-1.9 to -0.7, P < 0.001). There were no statistically significant differences between groups in other outcomes overall. However, participants at risk of or with type 2 diabetes experienced reductions in fasting glucose (-0.92 mmol L(-1), -1.22 to -0.62, P < 0.001) compared with CON. HIIT appears effective at improving metabolic health, particularly in those at risk of or with type 2 diabetes. Larger randomized controlled trials of longer duration than those included in this meta-analysis are required to confirm these results.
Clause sentiment identification based on convolutional neural network with context embedding

Identifying sentiment of opinion target is an essential component of many tasks for sentiment analysis. We firstly identify the sentiment of the clause in which the specific opinion target lie and then infer the sentiment of opinion target from the sentiment of clause. In order to utilize context more adequately, We propose a novel model using Long Short-Term Memory(LSTM) and Convolutional Neural Network(CNN) together to identify the sentiment of clause. LSTM is used for generating context embedding and CNN is treated as a trainable feature detector. In the experiment using product reviews data, our model outperforms traditional methods in the aspect of accuracy. What's more, the time of model training is acceptable and our model is more scalable because we don't need to discovery rules manually and prepare lots of external language resources which is laborious and time-consuming.
Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering

We introduce a new approach to hierarchy formation and task decomposition in hierarchical reinforcement learning. Our method is based on the Hierarchy Of Abstract Machines (HAM) framework because HAM approach is able to design efficient controllers that will realize specific behaviors in real robots. The key to our algorithm is the introduction of the internal or “mental” environment in which the state represents the structure of the HAM hierarchy. The internal action in this environment leads to changes the hierarchy of HAMs. We propose the classical Qlearning procedure in the internal environment which allows the agent to obtain an optimal hierarchy. We extends the HAM framework by adding on-model approach to select the appropriate sub-machine to execute action sequences for certain class of external environment states. Preliminary experiments demonstrated the prospects of the method.
Graphic logo detection with deep region-based convolutional networks

Logo detection is a challenging task with many practical applications in our daily life and intellectual property protection. The two main obstacles here are lack of public logo datasets and effective design of logo detection structure. In this paper, we first manually collected and annotated 6,400 images and mix them with FlickrLogo-32 dataset, forming a larger dataset. Secondly, we constructed Faster R-CNN frameworks with several widely used classification models for logo detection. Furthermore, the transfer learning method was introduced in the training process. Finally, clustering was used to guarantee suitable hyper-parameters and more precise anchors of RPN. Experimental results show that the proposed framework outper-forms the state of-the-art methods with a noticeable margin.
Analysis of PWM GTO Current Source Inverter-Fed Induction Motor Drive System

A GTO current source inverter which consists of six main GTO's, two auxiliary GTO's, and three capacitors is presented. This inverter can supply both the sinusoidal voltage and current to the motor by pulsewidth modulation (PWM) techniques. The normal PWM pattern produced by two control signals with the carrier and the modulating waves and the optimal PWM pattern determined by the harmonic analysis are described. The experimental waveforms for 2.2-kW induction motor drives are given and the circuit operation of this inverter in the PWM technique is clearly shown. In addition, the steady-state characteristics of this inverter-induction motor drive system are analyzed by the state-variable methods, and a close agreement between the analyzed and the experimental waveforms is obtained. It is shown that the harmonic components are eliminated or reduced by using the optimal PWM pattern, and the new inverter with sinusoidal current and voltage is very excellent for ac motor drive.
Growth rates of black soldier fly larvae fed on fresh human faeces and their implication for improving sanitation.

To determine the capacity of black soldier fly larvae (BSFL) (Hermetia illucens) to convert fresh human faeces into larval biomass under different feeding regimes, and to determine how effective BSFL are as a means of human faecal waste management.



Black soldier fly larvae were fed fresh human faeces. The frequency of feeding, number of larvae and feeding ratio were altered to determine their effects on larval growth, prepupal weight, waste reduction, bioconversion and feed conversion rate (FCR).



The larvae that were fed a single lump amount of faeces developed into significantly larger larvae and prepupae than those fed incrementally every 2 days; however, the development into pre-pupae took longer. The highest waste reduction was found in the group containing the most larvae, with no difference between feeding regimes. At an estimated 90% pupation rate, the highest bioconversion (16-22%) and lowest, most efficient FCR (2.0-3.3) occurred in groups that contained 10 and 100 larvae, when fed both the lump amount and incremental regime.



The prepupal weight, bioconversion and FCR results surpass those from previous studies into BSFL management of swine, chicken manure and municipal organic waste. This suggests that the use of BSFL could provide a solution to the health problems associated with poor sanitation and inadequate human waste management in developing countries.
Experimental Evaluation of Mutation Testing Approaches to Python Programs

Mutation testing of Python programs raises a problem of incompetent mutants. Incompetent mutants cause execution errors due to inconsistency of types that cannot be resolved before run-time. We present a practical approach in which incompetent mutants can be generated, but the solution is transparent for a user and incompetent mutants are detected by a mutation system during test execution. Experiments with 20 traditional and object-oriented operators confirmed that the overhead can be accepted. The paper presents an experimental evaluation of the first- and higher-order mutation. Four algorithms to the 2nd and 3rd order mutant generation were applied. The impact of code coverage consideration on the process efficiency is discussed. The experiments were supported by the MutPy system for mutation testing of Python programs.