nils-herrmann/clean_scirepeval huggingface dataset:
Full-mouth adhesive rehabilitation of a severely eroded dentition: the three-step technique. Part 3.

Dental erosion is a frequently underestimated pathology that nowadays affects an increasing number of younger individuals. Often the advanced tooth destruction is the result of not only a difficult initial diagnosis (e.g. multifactorial etiology of tooth wear), but also a lack of timely intervention. A clinical trial testing a fully adhesive approach for patients affected by severe dental erosion is underway at the School of Dental Medicine of the University of Geneva. All the patients are systematically and exclusively treated with adhesive techniques, using onlays in the posterior region and a combination of facially bonded porcelain restorations and indirect palatal resin composite restorations in the anterior maxillary region. To achieve maximum preservation of tooth structure and predict the most esthetic and functional outcome, an innovative concept has been developed: the three-step technique. Three laboratory steps are alternated with three clinical steps, allowing the clinician and the dental technician to constantly interact during the planning and execution of a full-mouth adhesive rehabilitation. In this article, the third and last step of the three-step technique has been described in detail.
Sensorimotor Robot Policy Training using Reinforcement Learning

Robots are becoming more ubiquitous in our society and taking over many tasks that were previously considered as human hallmarks. Many of these tasks, e.g., autonomously driving a car, collaborating with humans in dynamic and changing working conditions and performing household chores, require human-level intelligence to perceive the world and to act appropriately. In this thesis, we pursue a different approach compared to classical methods that often construct a robot controller based on the perception-then-action paradigm. We devise robotic action-selection policies by considering actionselection and perception processes as being intertwined, emphasizing that perception comes prior to action and action is key to perception. The main hypothesis is that complex robotic behaviors come as the result of mastering sensorimotor contingencies (SMCs), i.e., regularities between motor actions and associated changes in sensory observations, where SMCs can be seen as building blocks to skillful behaviors. We elaborate and investigate this hypothesis by deliberate design of frameworks which enable policy training merely based on data experienced by a robot, without intervention of human experts for analytical modelings or calibrations. In such circumstances, action policies can be obtained by reinforcement learning (RL) paradigm by making exploratory action decisions and reinforcing patterns of SMCs that lead to reward events for a given task. However, the dimensionality of sensorimotor spaces, complex dynamics of physical tasks, sparseness of reward events, limited amount of data from real-robot experiments, ambiguities of crediting past decisions and safety issues, which arise from exploratory actions of a physical robot, pose challenges to obtain a policy based on data-driven methods alone. In this thesis, we introduce our contributions to deal with the aforementioned issues by devising learning frameworks which endow a robot with the ability to integrate sensorimotor data to obtain action-selection policies. The effectiveness of the proposed frameworks is demonstrated by evaluating the methods on a number of real robotic tasks and illustrating the suitability of the methods to acquire different skills, to make sequential action-decisions in high-dimensional sensorimotor spaces, with limited data and sparse rewards.
Building a question answering test collection

The TREC-8 Question Answering (QA) Track was the first large-scale evaluation of domain-independent question answering systems. In addition to fostering research on the QA task, the track was used to investigate whether the evaluation methodology used for document retrieval is appropriate for a different natural language processing task. As with document relevance judging, assessors had legitimate differences of opinions as to whether a response actually answers a question, but comparative evaluation of QA systems was stable despite these differences. Creating a reusable QA test collection is fundamentally more difficult than creating a document retrieval test collection since the QA task has no equivalent to document identifiers.
An FPGA-Based Hardware Accelerator for Traffic Sign Detection

Traffic sign detection plays an important role in a number of practical applications, such as intelligent driver assistance and roadway inventory management. In order to process the large amount of data from either real-time videos or large off-line databases, a high-throughput traffic sign detection system is required. In this paper, we propose an FPGA-based hardware accelerator for traffic sign detection based on cascade classifiers. To maximize the throughput and power efficiency, we propose several novel ideas, including: 1) rearranged numerical operations; 2) shared image storage; 3) adaptive workload distribution; and 4) fast image block integration. The proposed design is evaluated on a Xilinx ZC706 board. When processing high-definition (1080p) video, it achieves the throughput of 126 frames/s and the energy efficiency of 0.041 J/frame.
Specification and Informational Issues in Credit Scoring

Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of “reject inference” – methods that use a rejected applicant’s characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model.
Dynamic tunnel switching for SDN-based cellular core networks

Future cellular networks are faced with the challenge of coping with significant traffic growth without increasing operating costs. Network virtualization and Software Defined Networking (SDN) are emerging solutions for fine-grained control and management of networks. In this article, we present a new dynamic tunnel switching technique for SDN-based cellular core networks. The technique introduces a virtualized Evolved Packet Core (EPC) gateway with the capability to select and dynamically switch the user plane processing element for each user. Dynamic GPRS Tunneling Protocol (GTP) termination enables switching the mobility anchor of an active session between a cloud environment, where general purpose hardware is in use, and a fast path implemented with dedicated hardware. We describe a prototype implementation of the technique based on an OpenStack cloud, an OpenFlow controller with GTP tunnel switching, and a dedicated fast path element.
Fast food for family meals: relationships with parent and adolescent food intake, home food availability and weight status.

The purpose of the present study was to examine the prevalence of fast-food purchases for family meals and the associations with sociodemographic variables, dietary intake, home food environment, and weight status in adolescents and their parents.



This study is a cross-sectional evaluation of parent interviews and adolescent surveys from Project EAT (Eating Among Teens).



Subjects included 902 middle-school and high-school adolescents (53% female, 47% male) and their parents (89% female, 11% male). The adolescent population was ethnically diverse: 29% white, 24% black, 21% Asian American, 14% Hispanic and 12% other.



Results showed that parents who reported purchasing fast food for family meals at least 3 times per week were significantly more likely than parents who reported purchasing fewer fast-food family meals to report the availability of soda pop and chips in the home. Adolescents in homes with fewer than 3 fast-food family meals per week were significantly more likely than adolescents in homes with more fast-food family meals to report having vegetables and milk served with meals at home. Fast-food purchases for family meals were positively associated with the intake of fast foods and salty snack foods for both parents and adolescents; and weight status among parents. Fast-food purchases for family meals were negatively associated with parental vegetable intake.



Fast-food purchases may be helpful for busy families, but families need to be educated on the effects of fast food for family meals and how to choose healthier, convenient family meals.
Qualitative Spatial Representation and Reasoning Techniques

The eld of Qualitative Spatial Reasoning is now an active research area in its own right within AI (and also in Geographical Information Systems) having grown out of earlier work in philosophical logic and more general Qualitative Reasoning in AI. In this paper (which is an updated version of 25]) I will survey the state of the art in Qualitative Spatial Reasoning, covering representation and reasoning issues as well as pointing to some application areas. 1 What is Qualitative Reasoning? The principal goal of Qualitative Reasoning (QR) 129] is to represent not only our everyday commonsense knowledge about the physical world, but also the underlying abstractions used by engineers and scientists when they create quantitative models. Endowed with such knowledge, and appropriate reasoning methods , a computer could make predictions, diagnoses and explain the behaviour of physical systems in a qualitative manner, even when a precise quantitative description is not available 1 or is computationally intractable. The key to a qualitative representation is not simply that it is symbolic, and utilises discrete quantity spaces, but that the distinctions made in these discretisations are relevant to the behaviour being modelled { i.e. distinctions are only introduced if they are necessary to model some particular aspect of the domain with respect to the task in hand. Even very simple quantity spaces can be very useful, e.g. the quantity space consisting just of f?; 0; +g, representing the two semi-open intervals of the real number line, and their dividing point, is widely used in the literature, e.g. 129]. Given such a quantity space, one then wants to be able to compute with it. There is normally a natural ordering (either partial or total) associated with a quantity space, and one form of simple but eeective inference 1 Note that although one use for qualitative reasoning is that it allows inferences to be made in the absence of complete knowledge, it does this not by probabilistic or fuzzy techniques (which may rely on arbitrarily assigned probabilities or membership values) but by refusing to diierentiate between quantities unless there is suucient evidence to do so; this is achieved essentially by collapsingìndistinguishable' values into an equivalence class which becomes a qualitative quantity. The case where the indistinguishability relation is not an equivalence relation has not been much considered, except by 86, 83].
Making CNC machine tools more open, interoperable and intelligent - a review of the technologies

The aim of the next generation of computer numerically controlled (CNC) machines is to be portable, interoperable and adaptable. Over the years, G-codes (ISO 6983) have been extensively used by the CNC machine tools for part programming and are now considered as a bottleneck for developing next generation of CNC machines. A new standard known as STEP-NC is being developed as the data model for a new breed of CNC machine tools. The data model represents a common standard specifically aimed at the intelligent CNC manufacturing workstation, making the goal of a standardised CNC controller and NC code generation facility a reality. It is believed that CNC machines implementing STEP-NC will be the basis for a more open and adaptable architecture. This paper outlines a futuristic view of STEP-NC to support distributed interoperable intelligent manufacturing through global networking with autonomous manufacturing workstations with STEP compliant data interpretation, intelligent part program generation, diagnostics and maintenance, monitoring and job production scheduling.
THE ROLE OF CULTURE IN TEACHING AND LEARNING OF ENGLISH AS A FOREIGN LANGUAGE

Language is a means of expression. We express our feelings, emotions, thoughts, needs, desires etc. in words, symbols and gesture which is considered as language. Language can be defined as verbal, physical, biologically innate, and a basic form of communication. Culture is the characteristics of a particular group of people, defined by everything from language, religion, cuisine, social habits, music and arts. Thus culture finds its expression in language; so, learning a new language without familiarity with its culture remains incomplete. An important question arises here, is it necessary to learn about the culture of the target language to acquire English as a foreign or second language? There are great discussions by many scholars and researchers on this topic for decades. This article aims at defining culture, its relationship with language and what role it plays in teaching and learning English as a foreign or second language. This also shed light on how to teach culture in English language classroom.
Forecasting of preprocessed daily solar radiation time series using neural networks

In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad-hoc time series preprocessing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE ~ 21% and RMSE ~ 3.59 MJ/m2. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data preprocessing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayes inferences. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41°55'N, 8°44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination ...) allow to predict the best daily DC PV power production at horizon d+1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R2 > 0.99 and nRMSE < 2%).
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris’ distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
Siamese Instance Search for Tracking

In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.
Split-Plot Designs : What , Why , and How

T HIS provocative remark has been attributed to the famous industrial statistician, Cuthbert Daniel, by Box et al. (2005) in their well-known text on the design of experiments. Split-Plot experiments were invented by Fisher (1925) and their importance in industrial experimentation has been long recognized (Yates (1936)). It is also well known that many industrial experiments are fielded as split-plot experiments and yet erroneously analyzed as if they were completely randomized designs. This is frequently the case when hard-to-change factors exist and economic constraints preclude the use of complete randomization. Recent work, most notably by Lucas and his coworkers (Anbari and Lucas (1994), Ganju and Lucas (1997, 1999, 2005), Ju and Lucas (2002), Webb et al. (2004)) has demonstrated that many experi-
Scrum of scrums solution for large size teams using scrum methodology

Scrum is a structured framework to support complex product development. However, Scrum methodology faces a challenge of managing large teams. To address this challenge, in this paper we propose a solution called Scrum of Scrums. In Scrum of Scrums, we divide the Scrum team into teams of the right size, and then organize them hierarchically into a Scrum of Scrums. The main goals of the proposed solution are to optimize communication between teams in Scrum of Scrums; to make the system work after integration of all parts; to reduce the dependencies between the parts of system; and to prevent the duplication of parts in the system.
Video Visual Relation Detection

As a bridge to connect vision and language, visual relations between objects in the form of relation triplet $łangle subject,predicate,object\rangle$, such as "person-touch-dog'' and "cat-above-sofa'', provide a more comprehensive visual content understanding beyond objects. In this paper, we propose a novel vision task named Video Visual Relation Detection (VidVRD) to perform visual relation detection in videos instead of still images (ImgVRD). As compared to still images, videos provide a more natural set of features for detecting visual relations, such as the dynamic relations like "A-follow-B'' and "A-towards-B'', and temporally changing relations like "A-chase-B'' followed by "A-hold-B''. However, VidVRD is technically more challenging than ImgVRD due to the difficulties in accurate object tracking and diverse relation appearances in video domain. To this end, we propose a VidVRD method, which consists of object tracklet proposal, short-term relation prediction and greedy relational association. Moreover, we contribute the first dataset for VidVRD evaluation, which contains 1,000 videos with manually labeled visual relations, to validate our proposed method. On this dataset, our method achieves the best performance in comparison with the state-of-the-art baselines.
Improvement Schemes for Indoor Mobile Location Estimation : A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hiddenMarkov models.Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.
ANN and rule based model for english to sanskrit machine translation

The development of Machine Translation system for ancient language such as Sanskrit language is much more fascinating and challenging task. Due to lack of linguistic community, there are no wide work accomplish in Sanskrit translation while it is mother language by virtue of its importance in cultural heritage of India. In this paper, we integrate a traditional rule based approach of machine translation with Artificial Neural Network (ANN) model which translates an English sentence (source language sentence) into equivalent Sanskrit sentence (target language sentence). We use feed forward ANN for the selection of Sanskrit word like noun, verb, object, adjective etc from English to Sanskrit User Data Vector (UDV). Due to morphological richness of Sanskrit language, this system makes limited use of syntax and uses only morphological markings to identify Subject, Object, Verb, Preposition, Adjective, Adverb and as well as Conjunctive sentences also. It uses limited parsing for part of speech (POS) tagging, identification of clause, its Subject, Object, Verb etc and Gender-Number-Person (GNP) of noun, adjective and object. This system represents the translation between the SVO and SOV classes of languages. This system gives translation result in GUI form and handles English sentences of different classes.