nils-herrmann/clean_scirepeval huggingface dataset:
Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases

During the last decade, multimedia databases have become increasingly important in many application areas such as medicine, CAD, geography, and molecular biology. An important research issue in the field of multimedia databases is the content-based retrieval of similar multimedia objects such as images, text, and videos. However, in contrast to searching data in a relational database, a content-based retrieval requires the search of similar objects as a basic functionality of the database system. Most of the approaches addressing similarity search use a so-called feature transformation that transforms important properties of the multimedia objects into high-dimensional points (feature vectors). Thus, the similarity search is transformed into a search of points in the feature space that are close to a given query point in the high-dimensional feature space. Query processing in high-dimensional spaces has therefore been a very active research area over the last few years. A number of new index structures and algorithms have been proposed. It has been shown that the new index structures considerably improve the performance in querying large multimedia databases. Based on recent tutorials [Berchtold and Keim 1998], in this survey we provide an overview of the current state of the art in querying multimedia databases, describing the index structures and algorithms for an efficient query processing in high-dimensional spaces. We identify the problems of processing queries in high-dimensional space, and we provide an overview of the proposed approaches to overcome these problems.
Enumerating the Non-Isomorphic Assembly Configurations of Modular Robotic Systems

A \modular" robotic system consists of joint and link modules that can be assembled in a variety of conngurations to meet diierent or changing task requirements. However, due to typical symmetries in module design, diierent assembly conngurations may lead to robotic structures which are kinematically identical, or isomorphic. This paper considers how to enumerate the non-isomorphic assembly conngurations of a modular robotic system. We introduce an Assembly Incidence Matrix (AIM) to represent a modular robot assembly con-guration. Then we use symmetries of the module geometry and graph isomorphisms to deene an equivalence relation on the AIMs. Equivalent AIMs represent isomorphic robot assembly conngurations. Based on this equivalence relation, we propose an algorithm to generate non-isomorphic assembly conngurations of an n-link tree-like robot with diierent joint and link module types. Examples demonstrate that this method is a signiicant improvement over a brute force enumeration process.
Vision-based localization and mapping for an autonomous mower

This paper presents a vision-based localization and mapping algorithm for an autonomous mower. We divide the task for robotic mowing into two separate phases, a teaching phase and a mowing phase. During the teaching phase, the mower estimates the 3D positions of landmarks and defines a boundary in the lawn with an estimate of its own trajectory. During the mowing phase, the location of the mower is estimated using the landmark and boundary map acquired from the teaching phase. Of particular interest for our work is ensuring that the estimator for landmark mapping will not fail due to the nonlinearity of the system during the teaching phase. A nonlinear observer is designed with pseudo-measurements of each landmark's depth to prevent the map estimator from diverging. Simultaneously, the boundary is estimated with an EKF. Measurements taken from an omnidirectional camera, an IMU, and a ground speed sensor are used for the estimation. Numerical simulations and offline teaching phase experiments with our autonomous mower demonstrate the potential of our algorithm.
Secure Access Control Scheme of RFID System Application

Radio Frequency Identification (RFID) is a contactless technology, it considered the way to replace the barcode, since the barcode is data read with line of sight and limits the utility for item-level of logistic and supply chain application in the future. RFID is intimate linking real and virtual also creates considerable security and privacy risk in RFID adoption. Until now, many researches on the RFID’s security and/or privacy were proposed. In this paper, we surveys the literature of hash-based access control scheme and propose an effective scheme to enhance the security and privacy about the passive RFID tag.
Unsupervised Learning of Narrative Event Chains

Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments. The second applies a temporal classifier to partially order the connected events. Finally, the third prunes and clusters self-contained chains from the space of events. We introduce two evaluations: the narrative cloze to evaluate event relatedness, and an order coherence task to evaluate narrative order. We show a 36% improvement over baseline for narrative prediction and 25% for temporal coherence.
Cryolipolysis for Targeted Fat Reduction and Improved Appearance of the Enlarged Male Breast.

Pseudogynecomastia refers to benign male breast enlargement due to excess subareolar fat. Standard treatment is surgical excision under general anesthesia, liposuction, or a combination of both.



The safety and efficacy of cryolipolysis was investigated for nonsurgical treatment of pseudogynecomastia.



Enrollment consisted of 21 males with pseudogynecomastia. Subjects received a first treatment consisting of a 60-minute cryolipolysis cycle, followed by a two-minute massage, and a second 60-minute cycle with 50% treatment area overlap. At 60 days of follow-up, subjects received a second 60-minute treatment. Safety was evaluated by monitoring side effects and adverse events. Efficacy was assessed by ultrasound, clinical photographs, and subject surveys.



Surveys revealed that 95% of subjects reported improved visual appearance and 89% reported reduced embarrassment associated with pseudogynecomastia. Ultrasound showed mean fat layer reduction of 1.6 ± 1.2 mm. Blinded reviewers correctly identified 82% of baseline photographs. Side effects included mild discomfort during treatment and transient paresthesia and tenderness. One case of paradoxical hyperplasia (PH) occurred but likelihood of PH in the male breast is not believed to be greater than in any other treatment area.



This study demonstrated feasibility of cryolipolysis for safe, effective, and well-tolerated nonsurgical treatment of pseudogynecomastia.
Speed profile generation based on quintic Bézier curves for enhanced passenger comfort

Automated ground vehicles are becoming a reality for future deployment due to their potential improvement of safety, comfort or emission reductions. However, some challenges remain unsolved such as navigation in dynamic urban environments, where safety and comfort are paramount. In this paper, a novel speed profile generator, based on quintic Bézier curves, is presented. This approach permits to improve the comfort in automated vehicles by constraining the global acceleration in the whole ride. Moreover, a comparison with a Jerk limitation algorithm for continuous acceleration profile and a smooth speed profile is performed. The quintic Bézier curves assure continuity of velocity curves and therefore a smooth Jerk and acceleration profiles. The ISO 2631-1 standard, for evaluation of human exposure to whole-body vibration, is considered in both methods for lateral and longitudinal comfort constraints. Simulations show that this approach improves comfort by smoothing the acceleration and jerk profiles, while also constraining the total acceleration perceived by the passengers.
Cross-language compiler benchmarking: are we fast yet?

Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts. 
 This paper presents 14 benchmarks and a novel methodology to assess the compiler effectiveness across language implementations. Using a set of common language abstractions, the benchmarks are implemented in Java, JavaScript, Ruby, Crystal, Newspeak, and Smalltalk. We show that the benchmarks exhibit a wide range of characteristics using language-agnostic metrics. Using four different languages on top of the same compiler, we show that the benchmarks perform similarly and therefore allow for a comparison of compiler effectiveness across languages. Based on anecdotes, we argue that these benchmarks help language implementers to identify performance bugs and optimization potential by comparing to other language implementations.
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks

In this paper, we propose an efficient algorithm for estimating the natural policy gradient using parameter-based exploration; this algorithm samples directly in the parameter space. Unlike previous methods based on natural gradients, our algorithm calculates the natural policy gradient using the inverse of the exact Fisher information matrix. The computational cost of this algorithm is equal to that of conventional policy gradients whereas previous natural policy gradient methods have a prohibitive computational cost. Experimental results show that the proposed method outperforms several policy gradient methods.
Implementation of potential field method for mobile robot navigation in greenhouse environment with WSN support

This paper deals with the implementation of mobile measuring station in greenhouse environment navigated using potential field method. The function of a greenhouse is to create the optimal growing conditions for the full life of the plants. Using autonomous measuring systems helps to monitor all the necessary parameters for creating the optimal environment in the greenhouse. The robot equipped with sensors is capable of driving to the end and back along crop rows inside the greenhouse. It introduces a wireless sensor network that was used for the purpose of measuring and controlling the greenhouse application. Continuous advancements in wireless technology and miniaturization have made the deployment of sensor networks to monitor various aspects of the environment increasingly flexible.
A comparison of issues and advantages in agile and incremental development between state of the art and an industrial case

Recent empirical studies have been conducted identifying a number of issues and advantages of incremental and agile methods. However, the majority of studies focused on one model (Extreme Programming) and small projects. To draw more general conclusions we conduct a case study in large scale development identifying issues and advantages, and compare the results with previous empirical studies on the topic. The principle results are that 1) the case study and literature agree on the benefits while new issues arise when using agile in large-scale, and 2) an empirical research framework is needed to make agile studies comparable.
Pop-up mars rover with textile-enhanced rigid-flex PCB body

This paper presents a novel manufacturing paradigm for constructing origami-inspired pop-up robots for future space exploration missions. The new approach uses a textile-enhanced rigid-flex printed circuit board (PCB) to implement a folding robot chassis using robust, spaceflight-tolerant materials, and integrates the robot electronics directly into the chassis for added compactness. The new approach also decouples the mechanical and electrical functions of the chassis flexures for improved kinematics and lifetime. This manufacturing paradigm was used to build PUFFER (Pop-Up Flat Folding Explorer Robot), a self-actuated pop-up rover being developed to provide a low-payload-cost mobility enhancement for future NASA missions.
Social influence on technology acceptance behavior: self-identity theory perspective

Social influence on technology acceptance behavior has been acknowledged but needs to be further articulated. While Subjective Norm (SN) has been dominantly used to capture the essence of social influence, the findings to date has led some researchers to question whether it captures the full extent of social influence. Recently, social psychologists have examined Self-Identity as a construct reflecting social influence on behavior. In particular, Self-Identity has been shown to have significant influence on voluntary behavior and have enduring effects, situations where the Subjective Norm had little effect. This study examines the effect of Self-Identity on technology acceptance decision in the context of a web-based class support system under the Technology Acceptance Model (TAM). The result demonstrates a significant direct and indirect effect of Self-Identity on technology acceptance. The result also confirms that Self-Identity has significant direct effect on the acceptance in voluntary and experienced situations, while Subjective Norm has no significant effect in both situations. Key implications for theory and practice are discussed.
Accelerating the mobile web with selective offloading

Mobile Web page loads are notoriously slow due to limited computing power and slow network access. Our preliminary experiments show that computation is a significant fraction of page load time on mobile devices. Also, energy arguments suggest that it will stay this way. To compensate the limited computing power, our position is that offloading portions of the page load process to the cloud can significantly improve mobile page load time. We propose a measurement-based framework that allows to offload portions of mobile page load process to the cloud. Unlike browsers that offload fixed parts of page loads such as Opera Mini, our framework will allow to offload any portion of the page load process. We will experiment with a large variety of real-world situations (e.g., varying computing power on mobile devices) by offloading varying portions of page loads using our framework. Informed by the experimental results, we will develop a mobile browser that considers the diverse situations as well as energy and data usage.
Vulnerability , risk and adaptation : A conceptual framework

The purpose of this paper is to present a tentative conceptual framework for studies of vulnerability and adaptation to climate variability and change, generally applicable to a wide range of contexts, systems and hazards. Social vulnerability is distinguished from biophysical vulnerability, which is broadly equivalent to the natural hazards concept of risk. The IPCC definition of vulnerability is discussed within this context, which helps us to reconcile apparently contradictory definitions of vulnerability. A concise typology of physically defined hazards is presented; the relationship between the vulnerability and adaptive capacity of a human system depends critically on the nature of the hazard faced. Adaptation by a system may be inhibited by process originating outside the system; it is therefore important to consider " external " obstacles to adaptation, and links across scales, when assessing adaptive capacity.
An Overview of Virtual Machine Architectures

hen early computer systems were being developed, hardware was designed first and software written specifically for that hardware followed. Each system was essentially handcrafted with its own instruction set, and software was developed for that specific instruction set. This included operating system software, assemblers (later compilers), and application programs. With a small user community, relatively simple programs, and fixed hardware, this tightly integrated approach worked quite well, especially while the basic concepts of the stored program computer were still evolving. But user communities grew, operating systems became more complex, and the number of application programs rapidly expanded, so that re-writing and distributing software for each new computer system became a major burden.
Training to enhance the physiological determinants of long-distance running performance: can valid recommendations be given to runners and coaches based on current scientific knowledge?

This article investigates whether there is currently sufficient scientific knowledge for scientists to be able to give valid training recommendations to long-distance runners and their coaches on how to most effectively enhance the maximal oxygen uptake, lactate threshold and running economy. Relatively few training studies involving trained distance runners have been conducted, and these studies have often included methodological factors that make interpretation of the findings difficult. For example, the basis of most of the studies was to include one or more specific bouts of training in addition to the runners' 'normal training', which was typically not described or only briefly described. The training status of the runners (e.g. off-season) during the study period was also typically not described. This inability to compare the runners' training before and during the training intervention period is probably the main factor that hinders the interpretation of previous training studies. Arguably, the second greatest limitation is that only a few of the studies included more than one experimental group. Consequently, there is no comparison to allow the evaluation of the relative efficacy of the particular training intervention. Other factors include not controlling the runners' training load during the study period, and employing small sample sizes that result in low statistical power. Much of the current knowledge relating to chronic adaptive responses to physical training has come from studies using sedentary individuals; however, directly applying this knowledge to formulate training recommendations for runners is unlikely to be valid. Therefore, it would be difficult to argue against the view that there is insufficient direct scientific evidence to formulate training recommendations based on the limited research. Although direct scientific evidence is limited, we believe that scientists can still formulate worthwhile training recommendations by integrating the information derived from training studies with other scientific knowledge. This knowledge includes the acute physiological responses in the various exercise domains, the structures and processes that limit the physiological determinants of long-distance running performance, and the adaptations associated with their enhancement. In the future, molecular biology may make an increasing contribution in identifying effective training methods, by identifying the genes that contribute to the variation in maximal oxygen uptake, the lactate threshold and running economy, as well as the biochemical and mechanical signals that induce these genes. Scientists should be cautious when giving training recommendations to runners and coaches based on the limited available scientific knowledge. This limited knowledge highlights that characterising the most effective training methods for long-distance runners is still a fruitful area for future research.
Story Cloze Ending Selection Baselines and Data Examination

This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using features based on word embeddings and semantic similarity computation. We further implement a neural LSTM system with different encoding strategies that try to model the relation between the story and the provided endings. Our experiments show that a model using representation features based on average word embedding vectors over the given story words and the candidate ending sentences words, joint with similarity features between the story and candidate ending representations performed better than the neural models. Our best model achieves an accuracy of 72.42, ranking 3rd in the official evaluation.
Plant/microbe cooperation for electricity generation in a rice paddy field

Soils are rich in organics, particularly those that support growth of plants. These organics are possible sources of sustainable energy, and a microbial fuel cell (MFC) system can potentially be used for this purpose. Here, we report the application of an MFC system to electricity generation in a rice paddy field. In our system, graphite felt electrodes were used; an anode was set in the rice rhizosphere, and a cathode was in the flooded water above the rhizosphere. It was observed that electricity generation (as high as 6 mW/m2, normalized to the anode projection area) was sunlight dependent and exhibited circadian oscillation. Artificial shading of rice plants in the daytime inhibited the electricity generation. In the rhizosphere, rice roots penetrated the anode graphite felt where specific bacterial populations occurred. Supplementation to the anode region with acetate (one of the major root-exhausted organic compounds) enhanced the electricity generation in the dark. These results suggest that the paddy-field electricity-generation system was an ecological solar cell in which the plant photosynthesis was coupled to the microbial conversion of organics to electricity.
Addressing two problems in deep knowledge tracing via prediction-consistent regularization

Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, through our extensive experimentation, we have noticed two major problems in the DKT model. The first problem is that the model fails to reconstruct the observed input. As a result, even when a student performs well on a KC, the prediction of that KC's mastery level decreases instead, and vice versa. Second, the predicted performance for KCs across time-steps is not consistent. This is undesirable and unreasonable because student's performance is expected to transit gradually over time. To address these problems, we introduce regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in prediction. Experiments show that the regularized loss function effectively alleviates the two problems without degrading the original task of DKT.1