nils-herrmann/clean_scirepeval huggingface dataset:
Rules and Ontology Based Data Access

In OBDA an ontology defines a high level global vocabulary for user queries, and such vocabulary is mapped to (typically relational) databases. Extending this paradigm with rules, e.g., expressed in SWRL or RIF, boosts the expressivity of the model and the reasoning ability to take into account features such as recursion and n-ary predicates. We consider evaluation of SPARQL queries under rules with linear recursion, which in principle is carried out by a 2-phase translation to SQL: (1) The SPARQL query, together with the RIF/SWRL rules, and the mappings is translated to a Datalog program, possibly with linear recursion; (2) The Datalog program is converted to SQL by using recursive common table expressions. Since a naive implementation of this translation generates inefficient SQL code, we propose several optimisations to make the approach scalable. We implement and evaluate the techniques presented here in the Ontop system. To the best of our knowledge, this results in the first system supporting all of the following W3C standards: the OWL 2 QL ontology language, R2RML mappings, SWRL rules with linear recursion, and SPARQL queries. The preliminary but encouraging experimental results on the NPD benchmark show that our approach is scalable, provided optimisations are applied.
Vision-Based Autonomous Robot Self-Docking and Recharging

This paper presents a method for autonomous recharging of a mobile robot, a necessity for achieving long-term robotic activity without human intervention. A recharging station is designed consisting of a stationary docking station and a docking mechanism mounted to an ER-1 Evolution Robotics robot. The docking station and docking mechanism serve as a dual-power source, providing a mechanical and electrical connection between the recharging system of the robot and a laptop placed on it. Docking strategy algorithms use vision based navigation. The result is a significantly low-cost, high-entrance angle tolerant system. Iterative improvements to the system, to resist environmental perturbations and implement obstacle avoidance, ultimately resulted in a docking success rate of 100 percent over 50 trials.
Automatic program repair with evolutionary computation

There are many methods for detecting and mitigating software errors but few generic methods for automatically repairing errors once they are discovered. This paper highlights recent work combining program analysis methods with evolutionary computation to automatically repair bugs in off-the-shelf legacy C programs. The method takes as input the buggy C source code, a failed test case that demonstrates the bug, and a small number of other test cases that encode the required functionality of the program. The repair procedure does not rely on formal specifications, making it applicable to a wide range of extant software for which formal specifications rarely exist.
Parkinson disease: systemic and orofacial manifestations, medical and dental management.

More than 1.5 million Americans have Parkinson disease (PD), and this figure is expected to rise as the population ages. However, the dental literature offers little information about the illness.


TYPES OF STUDIES REVIEWED
The authors conducted a MEDLINE search using the key terms "Parkinson's disease," "medical management" and "dentistry." They selected contemporaneous articles published in peer-reviewed journals and gave preference to articles reporting randomized controlled trials.



PD is a progressive neurodegenerative disorder caused by loss of dopaminergic and nondopaminergic neurons in the brain. These deficits result in tremor, slowness of movement, rigidity, postural instability and autonomic and behavioral dysfunction. Treatment consists of administering medications that replace dopamine, stimulate dopamine receptors and modulate other neurotransmitter systems.



Oral health may decline because of tremors, muscle rigidity and cognitive deficits. The dentist should consult with the patient's physician to establish the patient's competence to provide informed consent and to determine the presence of comorbid illnesses. Scheduling short morning appointments that begin 90 minutes after administration of PD medication enhances the patient's ability to cooperate with care. Inclination of the dental chair at 45 degrees, placement of a bite prop, use of a rubber dam and high-volume oral evacuation enhance airway protection. To avoid adverse drug interactions with levodopa and entacapone, the dentist should limit administration of local anesthetic agents to three cartridges of 2 percent lidocaine with 1:100,000 epinephrine per half hour, and patients receiving selegiline should not be given agents containing epinephrine or levonordefrin. The dentist should instruct the patient and the caregiver in good oral hygiene techniques.
Quarterly Time-Series Forecasting With Neural Networks

Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectiveness of several data preprocessing and modeling approaches. We consider two data preprocessing methods and 48 NN models with different possible combinations of lagged observations, seasonal dummy variables, trigonometric variables, and time index as inputs to the NN. Both parametric and nonparametric statistical analyses are performed to identify the best models under different circumstances and categorize similar models. Results indicate that simpler models, in general, outperform more complex models. In addition, data preprocessing especially with deseasonalization and detrending is very helpful in improving NN performance. Practical guidelines are also provided.
Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.
Bayesian analysis of neuroimaging data in FSL

Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.
Foundations of JSON Schema

JSON – the most popular data format for sending API requests and responses – is still lacking a standardized schema or meta-data definition that allows the developers to specify the structure of JSON documents.  Schema is an attempt to provide a general purpose schema language for JSON, but it is still work in progress, and the formal specification has not yet been agreed upon. Why this could be a problem becomes evident when examining the behaviour of numerous tools for validating JSON documents against this initial schema proposal: although they agree on most general cases, when presented with the greyer areas of the specification they tend to differ significantly. In this paper we provide the first formal definition of syntax and semantics for JSON Schema and use it to show that implementing this layer on top of JSON is feasible in practice. This is done both by analysing the theoretical aspects of the validation problem and by showing how to set up and validate a JSON Schema for Wikidata, the central storage for Wikimedia.
Deep Network Flow for Multi-object Tracking

Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples. A typical approach to data association involves finding a graph matching or network flow that minimizes a sum of pairwise association costs, which are often either hand-crafted or learned as linear functions of fixed features. In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs. We apply this approach to multi-object tracking with a network flow formulation. Our experiments demonstrate that we are able to successfully learn all cost functions for the association problem in an end-to-end fashion, which outperform hand-crafted costs in all settings. The integration and combination of various sources of inputs becomes easy and the cost functions can be learned entirely from data, alleviating tedious hand-designing of costs.
Ship trajectory prediction for intelligent traffic management using clustering and ANN

Yangtze River is the world's busiest inland waterway. Ships need to be guided when passing through controlled waterways based on their trajectory predictions. Inaccurate predicted trajectories lead to non-optimal traffic signalling which may cause significant traffic jam. For the existing intelligent traffic signalling systems (ITSSs), ships are supposed to travel exactly along the mid-line of the Yangtze River, which has caused many problems and issues. Over the last few years, traffic data have been growing exponentially, and we have gone into big data ear. This trend allows us to predict the ship travel trajectories using big historical data. In this paper, the historical trajectories are grouped by the popular K-Means clustering algorithm firstly, and then Artificial Neural Network (ANN) models are built using the above clustering results and other known factors (i.e. ship speed, loading capacity, self-weight, maximum power and water level) to predict the ships' trajectories. The experimental results show that the developed model is in good agreement with the actual data with more than 70% accuracy. It will also help to generate the optimal traffic commands for Yangtze River traffic control.
Mining Motifs in Massive Time Series Databases

The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns “motifs”, because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification. In this work we carefully motivate, then introduce, a nontrivial definition of time series motifs. We propose an efficient algorithm to discover them, and we demonstrate the utility and efficiency of our approach on several real world datasets.
Violence exposure in real-life, video games, television, movies, and the internet: is there desensitization?

It is believed that repeated exposure to real-life and to entertainment violence may alter cognitive, affective, and behavioral processes, possibly leading to desensitization. The goal of the present study was to determine if there are relationships between real-life and media violence exposure and desensitization as reflected in related characteristics. One hundred fifty fourth and fifth graders completed measures of real-life violence exposure, media violence exposure, empathy, and attitudes towards violence. Regression analyses indicated that only exposure to video game violence was associated with (lower) empathy. Both video game and movie violence exposure were associated with stronger proviolence attitudes. The active nature of playing video games, intense engagement, and the tendency to be translated into fantasy play may explain negative impact, though causality was not investigated in the present design. The samples' relatively low exposure to real-life violence may have limited the identification of relationships. Although difficult to quantify, desensitization to violence should be further studied using related characteristics as in the present study. Individual differences and causal relationships should also be examined.
Dynamic Graph Convolutional Networks

Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time. Our goal is to jointly exploit structured data and temporal information through the use of a neural network model. To the best of our knowledge, this task has not been addressed using these kind of architectures. For this reason, we propose two novel approaches, which combine Long Short-Term Memory networks and Graph Convolutional Networks to learn long short-term dependencies together with graph structure. The quality of our methods is confirmed by the promising results achieved.
Semantically-Guided Video Object Segmentation

This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame. One of the main challenges in this scenario is the change of appearance of the objects of interest. Their semantics, on the other hand, do not vary. This paper investigates how to take advantage of such invariance via the introduction of a semantic prior that guides the appearance model. Specifically, given the segmentation mask of the first frame of a sequence, we estimate the semantics of the object of interest, and propagate that knowledge throughout the sequence to improve the results based on an appearance model. We present Semantically-Guided Video Object Segmentation (SGV), which improves results over previous state of the art on two different datasets using a variety of evaluation metrics, while running in half a second per frame.
Antenna-in-Package and Transmit–Receive Switch for Single-Chip Radio Transceivers of Differential Architecture

A fully differential architecture from the antenna to the integrated circuit is proposed for radio transceivers in this paper. The physical implementation of the architecture into truly single-chip radio transceivers is described for the first time. Two key building blocks, the differential antenna and the differential transmit-receive (T-R) switch, were designed, fabricated, and tested. The differential antenna implemented in a package in low-temperature cofired-ceramic technology achieved impedance bandwidth of 2%, radiation efficiency of 84%, and gain of 3.2 dBi at 5.425 GHz in a size of 15 x 15 x 1.6 mm3. The differential T-R switch in a standard complementary metal-oxide-semiconductor technology achieved 1.8-dB insertion loss, 15-dB isolation, and 15-dBm 1-dB power compression point (P 1dB) without using additional techniques to enhance the linearity at 5.425 GHz in a die area of 60 x 40 mum2.
Verification of Embolic Channel Causing Blindness Following Filler Injection

Ocular complications following cosmetic filler injections are serious situations. This study provided scientific evidence that filler in the facial and the superficial temporal arteries could enter into the orbits and the globes on both sides. We demonstrated the existence of an embolic channel connecting the arterial system of the face to the ophthalmic artery. After the removal of the ocular contents from both eyes, liquid dye was injected into the cannulated channel of the superficial temporal artery in six soft embalmed cadavers and different color dye was injected into the facial artery on both sides successively. The interior sclera was monitored for dye oozing from retrograde ophthalmic perfusion. Among all 12 globes, dye injections from the 12 superficial temporal arteries entered ipsilateral globes in three and the contralateral globe in two arteries. Dye from the facial artery was infused into five ipsilateral globes and in three contralateral globes. Dye injections of two facial arteries in the same cadaver resulted in bilateral globe staining but those of the superficial temporal arteries did not. Direct communications between the same and different arteries of the four cannulated arteries were evidenced by dye dripping from the cannulating needle hubs in 14 of 24 injected arteries. Compression of the orbital rim at the superior nasal corner retarded ocular infusion in 11 of 14 arterial injections. Under some specific conditions favoring embolism, persistent interarterial anastomoses between the face and the eye allowed filler emboli to flow into the globe causing ocular complications. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .
SeGAN: Segmenting and Generating the Invisible

Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of completing the appearance of occluded objects. Doing so requires knowing which pixels to paint (segmenting the invisible parts of objects) and what color to paint them (generating the invisible parts). Our proposed novel solution, SeGAN, jointly optimizes for both segmentation and generation of the invisible parts of objects. Our experimental results show that: (a) SeGAN can learn to generate the appearance of the occluded parts of objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the invisible parts of objects; (c) trained on synthetic photo realistic images, SeGAN can reliably segment natural images; (d) by reasoning about occluder-occludee relations, our method can infer depth layering.
Intrusion detection techniques and approaches

Recent security incidents and analysis have demonstrated that manual response to such attacks is no longer feasible. Intrusion Detection systems offer techniques for modelling and recognising normal and abusive system behaviour. Such methodologies include: statistical models, immune system approaches, protocol verification, file and taint checking, neural networks, whitelisting, expression matching, state transition analysis, dedicated languages, genetic algorithms and burglar alarms. This paper describes these techniques including an IDS architectural outline and an analysis of IDS probe techniques finishing with a summary of associated technologies.
Obesity and the environment: where do we go from here?

The obesity epidemic shows no signs of abating. There is an urgent need to push back against the environmental forces that are producing gradual weight gain in the population. Using data from national surveys, we estimate that affecting energy balance by 100 kilocalories per day (by a combination of reductions in energy intake and increases in physical activity) could prevent weight gain in most of the population. This can be achieved by small changes in behavior, such as 15 minutes per day of walking or eating a few less bites at each meal. Having a specific behavioral target for the prevention of weight gain may be key to arresting the obesity epidemic.
The Alicia/sup 3/ climbing robot: a three-module robot for automatic wall inspection

The system proposed in this article is the Alicia/sup 3/ climbing robot. The aim of this project is to develop a system that can be adopted in a variety of applications, such as maintenance, building, inspection, and safety in the process and construction industries. The system could be adopted in many places where direct access by a human operator is very expensive because of the need for scaffolding or is very dangerous due to the presence of a hostile environment.
An iterative pruning algorithm for feedforward neural networks

The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper, a new pruning method is developed, based on the idea of iteratively eliminating units and adjusting the remaining weights in such a way that the network performance does not worsen over the entire training set. The pruning problem is formulated in terms of solving a system of linear equations, and a very efficient conjugate gradient algorithm is used for solving it, in the least-squares sense. The algorithm also provides a simple criterion for choosing the units to be removed, which has proved to work well in practice. The results obtained over various test problems demonstrate the effectiveness of the proposed approach.