nils-herrmann/clean_scirepeval huggingface dataset:
Assessing Learning Outcomes in Web Search: A Comparison of Tasks and Query Strategies

Users make frequent use of Web search for learning-related tasks, but little is known about how different Web search interaction strategies affect outcomes for learning-oriented tasks, or what implicit or explicit indicators could reliably be used to assess search-related learning on the Web. We describe a lab-based user study in which we investigated potential indicators of learning in web searching, effective query strategies for learning, and the relationship between search behavior and learning outcomes. Using questionnaires, analysis of written responses to knowledge prompts, and search log data, we found that searchers' perceived learning outcomes closely matched their actual learning outcomes; that the amount searchers wrote in post-search questionnaire responses was highly correlated with their cognitive learning scores; and that the time searchers spent per document while searching was also highly and consistently correlated with higher-level cognitive learning scores. We also found that of the three query interaction conditions we applied, an intrinsically diverse presentation of results was associated with the highest percentage of users achieving combined factual and conceptual knowledge gains. Our study provides deeper insight into which aspects of search interaction are most effective for supporting superior learning outcomes, and the difficult problem of how learning may be assessed effectively during Web search.
Regression Models for Ordinal Data : A Machine Learning Approach

In contrast to the standard machine learning tasks of classi fication and metric regression we investigate the problem of predicting var iables of ordinal scale, a setting referred to as ordinal regression. The task of ordinal regression arises frequently in the social sciences and in information retrie val where human preferences play a major role. Also many multi–class problems ar e really problems of ordinal regression due to an ordering of the classes. Alth oug the problem is rather novel to the Machine Learning Community it has been wi dely considered in Statistics before. All the statistical methods rely on a p robability model of a latent (unobserved) variable and on the condition of st chastic ordering . In this paper we develop a distribution independent formulation of the problem and give uniform bounds for our risk functional. The main difference to classification is the restriction that the mapping of objects to ranks must be tran sitive and asymmetric. Combining our theoretical framework with results from measurement theory we present an approach that is based on a mapping from objects to scalar utility values and thus guarantees transitivity and asymmetry. App lying the principle of Structural Risk Minimization as employed in Support Vector Machines we derive a new learning algorithm based on large margin rank boundari es for the task of ordinal regression. Our method is easily extended to nonlinea r utility functions. We give experimental results for an Information Retrieval tas k of learning the order of documents with respect to an initial query. Moreover, we sho w that our algorithm outperforms more naive approaches to ordinal regression su ch as Support Vector Classification and Support Vector Regression in the case of m ore than two ranks . 1This paper is a preliminary version of (Herbrich et al. 1999)
In Search of a Metric for Managing Architectural Technical Debt

Practices designed to expedite the delivery of stakeholder value can paradoxically lead to unexpected rework costs that ultimately degrade the flow of value over time. This is especially observable when features are developed based on immediate value, while dependencies that may slow down future development efforts are neglected. The technical debt metaphor conceptualizes this tradeoff between short-term and long-term value: taking shortcuts to optimize the delivery of features in the short term incurs debt, analogous to financial debt, that must be paid off later to optimize long-term success. In this paper, we describe taking an architecture-focused and measurement-based approach to develop a metric that assists in strategically managing technical debt. Such an approach can be used to optimize the cost of development over time while continuing to deliver value to the customer. We demonstrate our approach by describing its application to an ongoing system development effort.
Millimeter Wave Mobile Communications for 5 G Cellular : It Will Work !

The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices. INDEX TERMS 28GHz, 38GHz, millimeter wave propagation measurements, directional antennas, channel models, 5G, cellular, mobile communications, MIMO.
Applying autonomous sensor systems in logistics — Combining sensor networks , RFIDs and software agents

New sensor, communication and software technologies are used to broaden the facilities of tracing and tracing systems for food transports. n embedded assessing unit detects from sensor data collected by a wireless network potential risks for the freight quality. The estimation of he current maturing state of agricultural products will be supported by measurements of the gaseous hormone ethylene as an indicator for the ipening processes. A miniaturized high-resolution gas chromatography is under construction. The system autonomously configures itself to a roduct specific supervision task based on data scanned by an RFID reader during freight loading. Mobile software agents accompany the freight long the supply chain. They pre-process the vast sensor data and submit only substantial changes to the freight owner.
Compact CPW-Fed microstrip octagonal patch antenna with H slot for WLAN and WIMAX applications

In this paper, a Coplanar Wave Guide (CPW)-Fed microstrip octagonal patch antenna for WLAN and WIMAX Applications is proposed. The studied structure is suitable for 2.3/2.5/3.3/3.5/5/5.5GHz WiMAX and for 3.6/2.4–2.5/4.9–5.9GHz WLAN applications. The octagonal shape is obtained by making a cut in the four angles of the rectangular microstrip patch antenna; in addition the using of CPW-Fed allows obtaining UWB characteristics. The miniaturization in the antenna size for lower band is achieved by introducing an H slot in the radiating element. The proposed antenna is designed on a single and a small substrate board of dimensions 46×40×1.6 mm3. Moreover the miniaturized antenna has a good impedance matching and a reasonable gain. All the simulations were performed in CADFEKO, a Method of Moment (MoM) based solver.
Convolutional Neural Networks for No-Reference Image Quality Assessment

In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.
Dynamic Tangible User Interface Palettes

Graphics editors often suffer from a large number of tool palettes that compete with valuable document space. To address this problem and to bring back physical affordances similar to a painter’s palette, we propose to augment a digital tabletop with spatially tracked handheld displays. These displays are dynamically updated depending on their spatial location. We introduce the concept of spatial Work Zones that take up distinct 3D regions above the table surface and serve as physical containers for digital content that is organized as stacks of horizontal layers. Spatial Work Zones are represented either by physical objects or on-screen on the tabletop. Associated layers can be explored fluently by entering a spatial Work Zone with a handheld display. This provides quick access and seamless changes between tools and parts of the document that are instantly functional, i.e., ready to be used by a digital pen. We discuss several use cases illustrating our techniques and setting them into context with previous systems. Early user feedback indicates that combining dynamic GUI functionality with the physicality of spatially tracked handheld displays is promising and can be generalized beyond graphics editing.
A low-complexity pre-processing system for restoring low-quality QR code images

In this paper we propose a complete preprocessing system for restoring low-quality Quick Response code images. The target application for the system is to restore and decode a code image in a cell phone when the image is shown in another cell phone display. Therefore, the system design focuses on low complexity solutions that are effective for a variety of capturing and displaying devices. The system has been designed to deal with low contrast, blurred, rotated, and deformed code images, while keeping complexity low enough to run in real-time. The proposed system takes advantage of intrinsic Quick Response code characteristics in order to restore the code properly and efficiently. The system has been assessed on a realistic database composed of 533 images captured by a cell a phone from several different cell phone displays. After the proposed preprocessing, 97.4% of the images in the database were correctly decoded.
Advances in geometric modeling and feature extraction on pots

This paper outlines progress on the 3DK Knowledge and Distributed Intelligence project undertaken by the Partnership for Research In Stereo Modeling (PRISM) at Arizona State University. Three of the six 3DK pilot projects of this National Science Foundation funded project involve archaeological and biological material, namely ceramic vessels, lithic artifacts, and bones. These projects are: (1) “3D Morphology of Ceramic Vessels” which aims to learn about vessel uniformity and proportionality as indicators of developing craft specialization and complex social organization among prehistoric cultures; (2) “Lithic Refitting” which intends to develop the algorithms required to (partially) automate the refitting process through 3D scanning and surface modeling; and finally (3) “3D Topography of Joint Surfaces” which investigates the ability of human ancestors to make tools and walk upright by developing software that will automate segmentation of osteological features and quantify the surface area and curvature of joint surfaces and the congruency between reciprocal joint surfaces, allowing for the development of biomechanical models. This paper discusses the links between these three unique projects and explores the underlying research aspects common to all: geometric modeling, feature recognition, and the development of a database structure aimed at making 3D models of these artifacts available online for query.
A Unifying View of Geometry, Semantics, and Data Association in SLAM

Traditional approaches for simultaneous localization and mapping (SLAM) rely on geometric features such as points, lines, and planes to infer the environment structure. They make hard decisions about the (data) association between observed features and mapped landmarks to update the environment model. This paper makes two contributions to the state of the art in SLAM. First, it generalizes the purely geometric model by introducing semantically meaningful objects, represented as structured models of mid-level part features. Second, instead of making hard, potentially wrong associations between semantic features and objects, it shows that SLAM inference can be performed efficiently with probabilistic data association. The approach not only allows building meaningful maps (containing doors, chairs, cars, etc.) but also offers significant advantages in ambiguous environments.
Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives

In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.
A Simple Digital Power-Factor Correction Rectifier Controller

This paper introduces a single-phase digital power-factor correction (PFC) control approach that requires no input voltage sensing or explicit current-loop compensation, yet results in low-harmonic operation over a universal input voltage range and loads ranging from high-power operation in continuous conduction mode down to the near-zero load. The controller is based on low-resolution A/D converters and digital pulsewidth modulator, requires no microcontroller or DSP programming, and is well suited for a simple, low-cost integrated-circuit realization, or as a hardware description language core suitable for integration with other power control and power management functions. Experimental verification results are shown for a 300-W boost PFC rectifier.
Opening the can of worms: an exploration tool for vortical flows

Gaining a comprehensive understanding of turbulent flows still poses one of the great challenges in fluid dynamics. A well-established approach to advance this research is the analysis of the vortex structures contained in the flow. In order to be able to perform this analysis efficiently, supporting visualization tools with clearly defined requirements are needed. In this paper, we present a visualization system which matches these requirements to a large extent. The system consists of two components. The first component analyzes the flow by means of a novel combination of vortex core line detection and the /spl lambda//sub 2/ method. The second component is a vortex browser which allows for an interactive exploration and manipulation of the vortices detected and separated during the first phase. Our system improves the reliability and applicability of existing vortex detection methods and allows for a more efficient study of vortical flows which is demonstrated in an evaluation performed by experts.
Architectural Patterns Revisited - A Pattern Language

Architectural patterns are a key concept in the field of software architecture: they offer well-established solutions to architectural problems, help to document the architectural design decisions, facilitate communication between stakeholders through a common vocabulary, and describe the quality attributes of a software system as forces. Regrettably, finding and applying the appropriate architectural patterns in practice still remains largely ad-hoc and unsystematic. This is due to the lack of consensus in the community with respect to the “philosophy” and granularity of architectural patterns, as well as the lack of a coherent pattern language. In this paper we attempt to establish common ground in the architectural patterns community by proposing a pattern language that acts as a superset of the existing architectural pattern collections and categorizations. This language is particularly focused on establishing the relationships between the patterns and performs a categorization based on the concept of “architectural views”.
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.
Microwave-Based Noninvasive Concentration Measurements for Biomedical Applications

A Debye relaxation model for the permittivity of aqueous glucose solution is given up to 40 GHz. Measurements of aqueous solutions are compared with invasive measurements on over 40 blood samples. Reflection- and transmission-based techniques are explained, concerning the application in biomedical concentration measurements. Wideband measurements on different sensing structures are introduced. Reflection- and transmission-based sensors are applied to a six-port reflectometer, as well as a homodyne vector network analyzer.
A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes

In this paper we propose a convolutional neural network (CNN), which allows to identify corresponding patches of very high resolution (VHR) optical and SAR imagery of complex urban scenes. Instead of a siamese architecture as conventionally used in CNNs designed for image matching, we resort to a pseudo-siamese configuration with no interconnection between the two streams for SAR and optical imagery. The network is trained with automatically generated training data and does not resort to any hand-crafted features. First evaluations show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development to a generalized multi-sensor matching procedure.
Drugs of Abuse and Stress Trigger a Common Synaptic Adaptation in Dopamine Neurons

Drug seeking and drug self-administration in both animals and humans can be triggered by drugs of abuse themselves or by stressful events. Here, we demonstrate that in vivo administration of drugs of abuse with different molecular mechanisms of action as well as acute stress both increase strength at excitatory synapses on midbrain dopamine neurons. Psychoactive drugs with minimal abuse potential do not cause this change. The synaptic effects of stress, but not of cocaine, are blocked by the glucocorticoid receptor antagonist RU486. These results suggest that plasticity at excitatory synapses on dopamine neurons may be a key neural adaptation contributing to addiction and its interactions with stress and thus may be an attractive therapeutic target for reducing the risk of addiction.
Influence Maximization on Large-Scale Mobile Social Network: A Divide-and-Conquer Method

With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of “word-of-mouth”. It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g., to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile social network. In this paper, a divide-and-conquer strategy with parallel computing mechanism has been adopted. We first propose an algorithm called Community-based Greedy algorithm for mining top-K influential nodes. It encompasses two components: dividing the large-scale mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Then, to further improve the performance, we parallelize the influence propagation based on communities and consider the influence propagation crossing communities. Also, we give precision analysis to show approximation guarantees of our models. Experiments on real large-scale mobile social networks show that the proposed methods are much faster than previous algorithms, meanwhile, with high accuracy.
Penetration Testing with Improved Input Vector Identification

Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetration testing, as they indicate where an attack could be introduced and whether an attempted attack was successful. Current techniques for identifying input vectors and checking attack results are typically ad-hoc and incomplete, which can cause parts of an application to be untested and leave vulnerabilities undiscovered. In this paper, we propose a new approach to penetration testing that addresses these limitations by leveraging two recently-developed analysis techniques. The first is used to identify a web application's possible input vectors, and the second is used to automatically check whether an attack resulted in an injection. To empirically evaluate our approach, we compare it against a state-of-the-art, alternative technique. Our results show that our approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities.