nils-herrmann/clean_scirepeval huggingface dataset:
Agent-based modeling: An introduction and primer

Agents are self-contained objects within a software model that are capable of autonomously interacting with the environment and with other agents. Basing a model around agents (building an agent-based model, or ABM) allows the user to build complex models from the bottom up by specifying agent behaviors and the environment within which they operate. This is often a more natural perspective than the system-level perspective required of other modeling paradigms, and it allows greater flexibility to use agents in novel applications. This flexibility makes them ideal as virtual laboratories and testbeds, particularly in the social sciences where direct experimentation may be infeasible or unethical. ABMs have been applied successfully in a broad variety of areas, including heuristic search methods, social science models, combat modeling, and supply chains. This tutorial provides an introduction to tools and resources for prospective modelers, and illustrates ABM flexibility with a basic war-gaming example.
Evaluating machine learning models for engineering problems

The use of machine learning (ML), and in particular, artiicial neural networks (ANN), in engineering applications has increased dramatically over the last years. However, by and large, the development of such applications or their report lack proper evaluation. Deecient evaluation practice was observed in the general neural networks community and again in engineering applications through a survey we conducted of articles published in AI in Engineering and elsewhere. This deecient status hinders understanding and prevents progress. This paper goal is to remedy this situation. First, several evaluation methods are discussed with their relative qualities. Second, these qualities are illustrated by using the methods to evaluate ANN performance in two engineering problems. Third, a systematic evaluation procedure for ML is discussed. This procedure will lead to better evaluation of studies, and consequently to improved research and practice in the area of ML in engineering applications.
Real-Time Machine Learning: The Missing Pieces

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.
Distribution market clearing and settlement

There are various undergoing efforts by system operators to set up an electricity market at the distribution level to enable a rapid and widespread deployment of distributed energy resources (DERs) and microgrids. This paper follows the previous work of the authors in implementing the distribution market operator (DMO) concept, and focuses on investigating the clearing and settlement processes performed by the DMO. The DMO clears the market to assign the awarded power from the wholesale market to customers within its service territory based on their associated demand bids. The DMO accordingly settles the market to identify the distribution locational marginal prices (DLMPs) and calculate payments from each customer and the total payment to the system operator. Numerical simulations exhibit the merits and effectiveness of the proposed DMO clearing and settlement processes.
Dimensioning of Next Generation Networks Signaling Gateway for Improving a Quality of Service Target

In this paper, we propose a set of dimensioning rules, which deliver high quality session-based services over a Next Generation Network based IP/MPLS transport infrastructure. In particular, we develop a detailed dimensioning methodology for improving a target QoS requirement. The proposed methodology outlines an optimal equipment allocation strategy for a requested capacity. The benefits of operating a network under the paradigm of generous dimensioning, for converged multiservice traffic flows, include target QoS guarantee, scalability, and network resilience. We present and discuss experimental results which illustrate a practical implementation of the proposed dimensioning strategy and its benefits.
Teeth-Brushing Recognition Based on Deep Learning

In this paper, we propose a multi-stream deep learning framework to tackle the activity recognition problem of sixteen kinds of Bass brushing methods by brushing photos and sensor data. This is a challenge task because the model needs to infer the relevance between images and sensor data. In order to solve this problem, CNN model is exploited to learn the spatial features from images and the LSTM model is used to learn the temporal features from sensor data. Then, a fusion scheme is proposed for prediction. Experimental results show that our model achieves high accuracy by using both images and sensor data under the constraint that the dataset is still quite limited.
A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sentiment. We found that the sentiment of an emoticon is in substantial agreement with the sentiment of the entire tweet. Thus, emoticons are useful as predictors of tweet sentiment and should not be ignored in sentiment classification. However, the sentiment expressed by an emoticon agrees with the sentiment of the accompanying text only slightly better than random. Thus, using the text accompanying emoticons to train sentiment models is not likely to produce the best results, a fact that we show by comparing lexicons generated using emoticons with others generated using simple textual features.
Anatomy and Disorders of the Oral Cavity of Chinchillas and Degus.

Dental disease is among the most common causes for chinchillas and degus to present to veterinarians. Most animals with dental disease present with weight loss, reduced food intake/anorexia, and drooling. Degus commonly present with dyspnea. Dental disease has been primarily referred to as elongation and malocclusion of the cheek teeth. Periodontal disease, caries, and tooth resorption are common diseases in chinchillas, but are missed frequently during routine intraoral examination, even performed under general anesthesia. A diagnostic evaluation, including endoscopy-guided intraoral examination and diagnostic imaging of the skull, is necessary to detect oral disorders and to perform the appropriate therapy.
IndoNet: A Multilingual Lexical Knowledge Network for Indian Languages

We present IndoNet, a multilingual lexical knowledge base for Indian languages. It is a linked structure of wordnets of 18 different Indian languages, Universal Word dictionary and the Suggested Upper Merged Ontology (SUMO). We discuss various benefits of the network and challenges involved in the development. The system is encoded in Lexical Markup Framework (LMF) and we propose modifications in LMF to accommodate Universal Word Dictionary and SUMO. This standardized version of lexical knowledge base of Indian Languages can now easily be linked to similar global resources.
Public perceptions of biotechnology.

The very term 'Biotechnology' elicits a range of emotions, from wonder and awe to downright fear and hostility. This is especially true among non-scientists, particularly in respect of agricultural and food biotechnology. These emotions indicate just how poorly understood agricultural biotechnology is and the need for accurate, dispassionate information in the public sphere to allow a rational public debate on the actual, as opposed to the perceived, risks and benefits of agricultural biotechnology. This review considers first the current state of public knowledge on agricultural biotechnology, and then explores some of the popular misperceptions and logical inconsistencies in both Europe and North America. I then consider the problem of widespread scientific illiteracy, and the role of the popular media in instilling and perpetuating misperceptions. The impact of inappropriate efforts to provide 'balance' in a news story, and of belief systems and faith also impinges on public scientific illiteracy. Getting away from the abstract, we explore a more concrete example of the contrasting approach to agricultural biotechnology adoption between Europe and North America, in considering divergent approaches to enabling coexistence in farming practices. I then question who benefits from agricultural biotechnology. Is it only the big companies, or is it society at large--and the environment--also deriving some benefit? Finally, a crucial aspect in such a technologically complex issue, ordinary and intelligent non-scientifically trained consumers cannot be expected to learn the intricacies of the technology to enable a personal choice to support or reject biotechnology products. The only reasonable and pragmatic alternative is to place trust in someone to provide honest advice. But who, working in the public interest, is best suited to provide informed and accessible, but objective, advice to wary consumers?
Exploiting The Laws of Order in Smart Contracts

We investigate a family of bugs in blockchain-based smart contracts, which we call event-ordering (or EO) bugs. These bugs are intimately related to the dynamic ordering of contract events, i.e., calls of its functions on the blockchain, and enable potential exploits of millions of USD worth of Ether. Known examples of such bugs and prior techniques to detect them have been restricted to a small number of event orderings, typicall 1 or 2. Our work provides a new formulation of this general class of EO bugs as finding concurrency properties arising in long permutations of such events. The technical challenge in detecting our formulation of EO bugs is the inherent combinatorial blowup in path and state space analysis, even for simple contracts. We propose the first use of partial-order reduction techniques, using happen-before relations extracted automatically for contracts, along with several other optimizations built on a dynamic symbolic execution technique. We build an automatic tool called ETHRACER that requires no hints from users and runs directly on Ethereum bytecode. It flags 7-11% of over ten thousand contracts analyzed in roughly 18.5 minutes per contract, providing compact event traces that human analysts can run as witnesses. These witnesses are so compact that confirmations require only a few minutes of human effort. Half of the flagged contracts have subtle EO bugs, including in ERC-20 contracts that carry hundreds of millions of dollars worth of Ether. Thus, ETHRACER is effective at detecting a subtle yet dangerous class of bugs which existing tools miss.
Cost Analysis of On-Premise versus Cloud-hosted Implementation in Sub-Saharan Countries Research Paper

The cost of acquiring, managing, and maintaining ICT infrastructure is one of the main factors that hinder educational institutions in Sub-Saharan countries to adopt and implement eLearning. Recently, cloud computing has emerged as a new computing paradigm for delivering cost effective computing services that can be used to harness eLearning. However, the adoption of cloud computing in higher education in Sub-Saharan countries is very low. Although there are many factors that may influence educational institutions to adopt cloud services, cost effectiveness is often a key factor. Far too little is known on how much the use of cloud computing can be cost effective in delivering eLearning services. This paper compares the cost of hosting eLearning services between on-premise and cloud-hosted approaches in higher education, taking Tanzania as a case study. The study found that institutions can significantly reduce the cost of eLearning implementation by adopting a cloud-hosted approach. The findings of this study serve as a base for educational institutions seeking cost effective alternatives to implement eLearning in developing countries.
Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation

We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. Fluid annotation is based on three principles:(I) Strong Machine-Learning aid. We start from the output of a strong neural network model, which the annotator can edit by correcting the labels of existing regions, adding new regions to cover missing objects, and removing incorrect regions.The edit operations are also assisted by the model.(II) Full image annotation in a single pass. As opposed to performing a series of small annotation tasks in isolation [51,68], we propose a unified interface for full image annotation in a single pass.(III) Empower the annotator.We empower the annotator to choose what to annotate and in which order. This enables concentrating on what the ma-chine does not already know, i.e. putting human effort only on the errors it made. This helps using the annotation budget effectively.
 Through extensive experiments on the COCO+Stuff dataset [11,51], we demonstrate that Fluid Annotation leads to accurate an-notations very efficiently, taking 3x less annotation time than the popular LabelMe interface [70].
Phenotype and genotype in 52 patients with Rubinstein-Taybi syndrome caused by EP300 mutations.

Rubinstein-Taybi syndrome (RSTS) is a developmental disorder characterized by a typical face and distal limbs abnormalities, intellectual disability, and a vast number of other features. Two genes are known to cause RSTS, CREBBP in 60% and EP300 in 8-10% of clinically diagnosed cases. Both paralogs act in chromatin remodeling and encode for transcriptional co-activators interacting with >400 proteins. Up to now 26 individuals with an EP300 mutation have been published. Here, we describe the phenotype and genotype of 42 unpublished RSTS patients carrying EP300 mutations and intragenic deletions and offer an update on another 10 patients. We compare the data to 308 individuals with CREBBP mutations. We demonstrate that EP300 mutations cause a phenotype that typically resembles the classical RSTS phenotype due to CREBBP mutations to a great extent, although most facial signs are less marked with the exception of a low-hanging columella. The limb anomalies are more similar to those in CREBBP mutated individuals except for angulation of thumbs and halluces which is very uncommon in EP300 mutated individuals. The intellectual disability is variable but typically less marked whereas the microcephaly is more common. All types of mutations occur but truncating mutations and small rearrangements are most common (86%). Missense mutations in the HAT domain are associated with a classical RSTS phenotype but otherwise no genotype-phenotype correlation is detected. Pre-eclampsia occurs in 12/52 mothers of EP300 mutated individuals versus in 2/59 mothers of CREBBP mutated individuals, making pregnancy with an EP300 mutated fetus the strongest known predictor for pre-eclampsia.
mTOR and cancer: many loops in one pathway.

The mammalian target of rapamycin (mTOR) is a master regulator of cell growth and division that responds to a variety of stimuli, including nutrient, energy, and growth factors. In the last years, a significant number of pieces have been added to the puzzle of how mTOR coordinates and executes its functions. Extensive research on mTOR has also uncovered a complex network of regulatory loops that impact the therapeutic approaches aimed at targeting mTOR.
Numerical Solution of Fuzzy Stochastic Differential Equation

In this paper an alternative approach to solve uncertain Stochastic Differential Equation (SDE) is proposed. This uncertainty occurs due to the involved parameters in system and these are considered as Triangular Fuzzy Numbers (TFN). Here the proposed fuzzy arithmetic in  is used as a tool to handle Fuzzy Stochastic Differential Equation (FSDE). In particular, a system of Ito stochastic differential equations is analysed with fuzzy parameters. Further exact and Euler Maruyama approximation methods with fuzzy values are demonstrated and solved some standard SDE.
ESD protection design for differential low-noise amplifier with cross-coupled SCR

A new electrostatic discharge (ESD) protection scheme for differential low-noise amplifier (LNA) was proposed in this paper. The new ESD protection scheme, which evolved from the conventional double-diode ESD protection scheme without adding any extra device, was realized with cross-coupled silicon-controlled rectifier (SCR). With the new ESD protection scheme, the pin-to-pin ESD robustness can be improved, which was the most critical ESD-test pin combination for differential input pads. Experimental results had shown that differential LNA with cross-coupled-SCR ESD protection scheme can achieve excellent ESD robustness and good RF performances.
Kinodynamic trajectory optimization and control for car-like robots

This paper presents a novel generic formulation of Timed-Elastic-Bands for efficient online motion planning of car-like robots. The planning problem is defined in terms of a finite-dimensional and sparse optimization problem subject to the robots kinodynamic constraints and obstacle avoidance. Control actions are implicitly included in the optimized trajectory. Reliable navigation in dynamic environments is accomplished by augmenting the inner optimization loop with state feedback. The predictive control scheme is real-time capable and responds to obstacles within the robot's perceptual field. Navigation in large and complex environments is achieved in a pure pursuit fashion by requesting intermediate goals from a global planner. Requirements on the initial global path are fairly mild, compliance with the robot kinematics is not required. A comparative analysis with Reeds and Shepp curves and investigation of prototypical car maneuvers illustrate the advantages of the approach.
Ringing ears: the neuroscience of tinnitus.

Tinnitus is a phantom sound (ringing of the ears) that affects quality of life for millions around the world and is associated in most cases with hearing impairment. This symposium will consider evidence that deafferentation of tonotopically organized central auditory structures leads to increased neuron spontaneous firing rates and neural synchrony in the hearing loss region. This region covers the frequency spectrum of tinnitus sounds, which are optimally suppressed following exposure to band-limited noise covering the same frequencies. Cross-modal compensations in subcortical structures may contribute to tinnitus and its modulation by jaw-clenching and eye movements. Yet many older individuals with impaired hearing do not have tinnitus, possibly because age-related changes in inhibitory circuits are better preserved. A brain network involving limbic and other nonauditory regions is active in tinnitus and may be driven when spectrotemporal information conveyed by the damaged ear does not match that predicted by central auditory processing.
PREDICTING THE FINANCIAL SUCCESS OF HOLLYWOOD MOVIES USING AN INFORMATION FUSION APPROACH

Hollywood has often been called the land of hunches and wild guesses. The uncertainty associated with the predictability of product demand makes the movie business a risky endeavor. Therefore, predicting the box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, with a rather large and feature rich dataset, we explored the use of data mining methods (e.g., artificial neural networks, decision trees and support vector machines along with information fusion based ensembles) to predict the financial performance of a movie at the box-office before its theatrical release. In our prediction models, we have converted the forecasting problem into a classification problem—rather than forecasting the point estimate of box-office receipts; we classified a movie (based on its box-office receipts) into nine categories, ranging from a “flop” to a “blockbuster.” Herein we present our exciting prediction results where we compared individual models to those of the ensamples.
A Simple Approach to Ordinal Classification

Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications the class values do exhibit a natural order—for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this method is that it can only be applied in conjunction with a regression scheme. In this paper we present a simple method that enables standard classification algorithms to make use of ordering information in class attributes. By applying it in conjunction with a decision tree learner we show that it outperforms the naive approach, which treats the class values as an unordered set. Compared to special-purpose algorithms for ordinal classification our method has the advantage that it can be applied without any modification to the underlying learning scheme.
Managing injuries to the primary dentition.

This article overviews the diagnosis and management of traumatic injuries to primary teeth. The child's age, ability to cooperate for treatment, and the potential for collateral damage to developing permanent teeth can complicate the management of these injuries. The etiology of these injuries is reviewed including the disturbing role of child abuse. Serious medical complications including head injury, cervical spine injury, and tetanus are discussed. Diagnostic methods and the rationale for treatment of luxation injuries, crown, and crown/root fractures are included. Treatment priorities should include adequate pain control, safe management of the child's behavior, and protection of the developing permanent teeth.