nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
An Algorithm for Rendering Generalized Depth of Field Effects Based on Simulated Heat Diffusion

Depth of field is the swath through a 3D scene that is imaged in acceptable focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the laws of physics and by physical constraints. The depth of field effect has been simulated in computer graphics, but with the same limited control as found in real camera lenses. In this report, we use anisotropic diffusion to generalize depth of field in computer graphics by allowing the user to independently specify the degree of blur at each point in three-dimensional space. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to pick objects out of a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. Our algorithm operates by blurring a sequence of nonplanar layers that form the scene. Choosing a suitable blur algorithm for the layers is critical; thus, we develop appropriate blur semantics such that the blur algorithm will properly generalize depth of field. We found that anisotropic diffusion is the process that best suits these semantics.
Decision tree based Support Vector Machine for Intrusion Detection

Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems in Intrusion Detection Systems (IDS). This method can decrease the training and testing time of the IDS, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying two decision tree approaches: Hierarchical multiclass SVM and Tree structured multiclass SVM, to construct multiclass intrusion detection system.
An energy-efficient charge recycling approach for a SAR converter with capacitive DAC

A new method for switching the capacitors in the DAC capacitor array of a successive approximation register (SAR) ADC is presented. By splitting the MSB capacitor into b - 1 binary scaled sub-capacitors, the average switching energy of the array can be reduced by 37% compared to a conventional switching method. A formal solution of the switching energy in four different switching methods is included, and the equations are verified using HSPICE simulations of a 10b capacitor array in a 0.18 /spl mu/m CMOS process.
Future E-Enabled Aircraft Communications and Security: The Next 20 Years and Beyond

Aircraft data communications and networking are key enablers for civilian air transportation systems to meet projected aviation demands of the next 20 years and beyond. In this paper, we show how the envisioned e-enabled aircraft plays a central role in streamlining system modernization efforts. We show why performance targets such as safety, security, capacity, efficiency, environmental benefit, travel comfort, and convenience will heavily depend on communications, networking and cyber-physical security capabilities of the e-enabled aircraft. The paper provides a comprehensive overview of the state-of-the-art research and standardization efforts. We highlight unique challenges, recent advances, and open problems in enhancing operations as well as certification of the future e-enabled aircraft.
The Periodic Moving Average Filter for Removing Motion Artifacts from PPG Signals

The measurement accuracy for heart rate or SpO2 using photoplethysmography (PPG) is influenced by how well the noise from motion artifacts and other sources can be removed. Eliminating the motion artifacts is particularly difficult since its frequency band overlaps that of the basic PPG signal. Therefore, we propose the Periodic Moving Average Filter (PMAF) to remove motion artifacts. The PMAF is based on the quasi-periodicity of the PPG signals. After segmenting the PPG signal on periodic boundaries, we average the m samples of each period. As a result, we remove the motion artifacts well without the deterioration of the characteristic point.
Unsupervised Domain Adaptation with Residual Transfer Networks

The recent success of deep neural networks relies on massive amounts of labeled data. For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. In this paper, we propose a new approach to domain adaptation in deep networks that can jointly learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeled data in the target domain. We relax a shared-classifier assumption made by previous methods and assume that the source classifier and target classifier differ by a residual function. We enable classifier adaptation by plugging several layers into deep network to explicitly learn the residual function with reference to the target classifier. We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation. The adaptation can be achieved in most feed-forward models by extending them with new residual layers and loss functions, which can be trained efficiently via back-propagation. Empirical evidence shows that the new approach outperforms state of the art methods on standard domain adaptation benchmarks.
Asymmetric and Context-Dependent Semantic Similarity among Ontology Instances

In this paper we propose an asymmetric semantic similarity among instances within an ontology. We aim to define a measurement of semantic similarity that exploit as much as possible the knowledge stored in the ontology taking into account different hints hidden in the ontology definition. The proposed similarity measurement considers different existing similarities, which we have combined and extended. Moreover, the similarity assessment is explicitly parameterised according to the criteria induced by the context. The parameterisation aims to assist the user in the decision making pertaining to similarity evaluation, as the criteria can be refined according to user needs. Experiments and an evaluation of the similarity assessment are presented showing the efficiency of the method.
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

ÐRetrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation that provides a transformation from the raw pixel data to a small set of image regions that are coherent in color and texture. This aBlobworldo representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection. An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics. By finding image regions that roughly correspond to objects, we allow querying at the level of objects rather than global image properties. We present results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects. Index TermsÐSegmentation and grouping, image retrieval, image querying, clustering, Expectation-Maximization.
A Semantic Approach to Discovering Schema Mapping Expressions

In many applications it is important to find a meaningful relationship between the schemas of a source and target database. This relationship is expressed in terms of declarative logical expressions called schema mappings. The more successful previous solutions have relied on inputs such as simple element correspondences between schemas in addition to local schema constraints such as keys and referential integrity. In this paper, we investigate the use of an alternate source of information about schemas, namely the presumed presence of semantics for each table, expressed in terms of a conceptual model (CM) associated with it. Our approach first compiles each CM into a graph and represents each table's semantics as a subtree in it. We then develop algorithms for discovering subgraphs that are plausible connections between those concepts/nodes in the CM graph that have attributes participating in element correspondences. A conceptual mapping candidate is now a pair of source and target subgraphs which are semantically similar. At the end, these are converted to expressions at the database level. We offer experimental results demonstrating that, for test cases of non-trivial mapping expressions involving schemas from a number of domains, the "semantic" approach outperforms the traditional technique in terms of recall and especially precision.
Miniature 3-Axis Distal Force Sensor for Minimally Invasive Surgical Palpation

Minimally invasive surgery (MIS) is a surgical technique that offers distinct advantages in reducing pain and patients' recovery time. However, the drawback due to the lack of force and tactile feedback presents a great deal of limitations in MIS procedures. Tissue palpation, which is easily conducted during traditional open surgery to examine tissue properties and abnormalities, is not possible when performing surgery in a minimally invasive manner. This paper proposes a specially designed miniature 3-axis distal force sensor that can be used to perform tissue palpation, measuring tissue interaction forces at the tip of a surgical instrument. Relying on an optical sensing scheme, the sensor can measure forces within measurement ranges of ±3 N in axial direction and ±1.5 N in radial direction. The resolution is 0.02 N. It is compatible with laparoscopic operations and can be used to localize tissue lesions or relatively hard nodules buried under an organ's surface, which are not detectable by visual means.
Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man

We present an application of Monte Carlo tree search (MCTS) for the game of Ms Pac-Man. Contrary to most applications of MCTS to date, Ms Pac-Man requires almost real-time decision making and does not have a natural end state. We approached the problem by performing Monte Carlo tree searches on a five player maxn tree representation of the game with limited tree search depth. We performed a number of experiments using both the MCTS game agents (for pacman and ghosts) and agents used in previous work (for ghosts). Performance-wise, our approach gets excellent scores, outperforming previous non-MCTS opponent approaches to the game by up to two orders of magnitude.
Optimal Design and Tradeoff Analysis of Planar Transformer in High-Power DC–DC Converters

The trend toward high power density, high operating frequency, and low profile in power converters has exposed a number of limitations in the use of conventional wire-wound magnetic component structures. A planar magnetic is a low-profile transformer or inductor utilizing planar windings, instead of the traditional windings made of Cu wires. In this paper, the most important factors for planar transformer (PT) design including winding loss, core loss, leakage inductance, and stray capacitance have individually been investigated. The tradeoffs among these factors have to be analyzed in order to achieve optimal parameters. Combined with an application, four typical winding arrangements have been compared to illustrate their advantages and disadvantages. An improved interleaving structure with optimal behaviors is proposed, which constructs the top layer paralleling with the bottom layer and then in series with the other turns of the primary, so that a lower magnetomotive force ratio m can be obtained, as well as minimized ac resistance, leakage inductance, and even stray capacitance. A 1.2-kW full-bridge dc-dc converter prototype employing the improved PT structure has been constructed, over 96% efficiency is achieved, and a 2.7% improvement, compared with the noninterleaving structure, is obtained.
A survey of motion-parallax-based 3-D reconstruction algorithms

The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibration of the camera is required. Under each category, related algorithms are further grouped according to the common properties they share.
The costs and benefits of providing feedback during learning.

It seems uncontroversial that providing feedback after a test, in the form of the correct answer, enhances learning. In real-world educational situations, however, the time available for learning is often constrained-and feedback takes time. We report an experiment in which total time for learning was fixed, thereby creating a trade-off between spending time receiving feedback and spending time on other learning activities. Our results suggest that providing feedback is not universally beneficial. Indeed, under some circumstances, taking time to provide feedback can have a negative net effect on learning. We also found that learners appear to have some insight about the costs of feedback; when they were allowed to control feedback, they often skipped unnecessary feedback in favor of additional retrieval attempts, and they benefited from doing so. These results underscore the importance of considering the costs and benefits of interventions designed to enhance learning.
Web page classification: Features and algorithms

Classification of Web page content is essential to many tasks in Web information retrieval such as maintaining Web directories and focused crawling. The uncontrolled nature of Web content presents additional challenges to Web page classification as compared to traditional text classification, but the interconnected nature of hypertext also provides features that can assist the process.
 As we review work in Web page classification, we note the importance of these Web-specific features and algorithms, describe state-of-the-art practices, and track the underlying assumptions behind the use of information from neighboring pages.
Critical evidence: a test of the critical-period hypothesis for second-language acquisition.

The critical-period hypothesis for second-language acquisition was tested on data from the 1990 U.S. Census using responses from 2.3 million immigrants with Spanish or Chinese language backgrounds. The analyses tested a key prediction of the hypothesis, namely, that the line regressing second-language attainment on age of immigration would be markedly different on either side of the critical-age point. Predictions tested were that there would be a difference in slope, a difference in the mean while controlling for slope, or both. The results showed large linear effects for level of education and for age of immigration, but a negligible amount of additional variance was accounted for when the parameters for difference in slope and difference in means were estimated. Thus, the pattern of decline in second-language acquisition failed to produce the discontinuity that is an essential hallmark of a critical period.
Houdini, an Annotation Assistant for ESC/Java

A static program checker that performs modular checking can check one program module for errors without needing to analyze the entire program. Modular checking requires that each module be accompanied by annotations that specify the module. To help reduce the cost of writing specifications, this paper presents Houdini, an annotation assistant for the modular checker ESC/Java. To infer suitable ESC/Java annotations for a given program, Houdini generates a large number of candidate annotations and uses ESC/Java to verify or refute each of these annotations. The paper describes the design, implementation, and preliminary evaluation of Houdini.
Pictorial Illustrations Still Improve Students ’ Learning From Text

Research conducted primarily during the 1970s and 1980s supported the assertion that carefully constructed text illustrations generally enhance learners’ performance on a variety of text-dependent cognitive outcomes. Research conducted throughout the 1990s still strongly supports that assertion. The more recent research has extended pictures-in-text conclusions to alternative media and technological formats and has begun to explore more systematically the “whys,” “whens,” and “for whoms” of picture facilitation, in addition to the “whethers” and “how muchs.” Consideration is given here to both more and less conventional types of textbook illustration, with several “tenets for teachers” provided in relation to each type.
Trust and e-commerce: a study of consumer perceptions

Increasing use of the World Wide Web as a B2C commercial tool raises interest in understanding the key issues in building relationships with customers on the Internet. Trust is believed to be the key to these relationships. Given the differences between a virtual and a conventional marketplace, antecedents and consequences of trust merit re-examination. This research identifies a number of key factors related to trust in the B2C context and proposes a framework based on a series of underpinning relationships among these factors. The findings in this research suggest that people are more likely to purchase from the web if they perceive a higher degree of trust in e-commerce and have more experience in using the web. Customer’s trust levels are likely to be influenced by the level of perceived market orientation, site quality, technical trustworthiness, and user’s web experience. People with a higher level of perceived site quality seem to have a higher level of perceived market orientation and trustworthiness towards e-commerce. Furthermore, people with a higher level of trust in e-commerce are more likely to participate in e-commerce. Positive ‘word of mouth’, money back warranty and partnerships with well-known business partners, rank as the top three effective risk reduction tactics. These findings complement the previous findings on e-commerce and shed light on how to establish a trust relationship on the World Wide Web.  2003 Elsevier B.V. All rights reserved.
Efficient 2D and 3D Facade Segmentation Using Auto-Context

This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference.
A general path-based representation for predicting program properties

Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a way that facilitates effective learning. 
 We present a general path-based representation for learning from programs. Our representation is purely syntactic and extracted automatically. The main idea is to represent a program using paths in its abstract syntax tree (AST). This allows a learning model to leverage the structured nature of code rather than treating it as a flat sequence of tokens. 
 We show that this representation is general and can: (i) cover different prediction tasks, (ii) drive different learning algorithms (for both generative and discriminative models), and (iii) work across different programming languages. 
 We evaluate our approach on the tasks of predicting variable names, method names, and full types. We use our representation to drive both CRF-based and word2vec-based learning, for programs of four languages: JavaScript, Java, Python and C#. Our evaluation shows that our approach obtains better results than task-specific handcrafted representations across different tasks and programming languages.