nils-herrmann/clean_scirepeval huggingface dataset:
A robotic excavator for autonomous truck loading

Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focussed on selected parts of the problem. In this paper, we present a system that completely automates the truck loading task. The excavator uses two scanning laser rangefinders to recognize and localize the truck, measure the soil face, and detect obstacles. The excavator’s software decides where to dig in the soil, where to dump in the truck, and how to quickly move between these points while detecting and stopping for obstacles. The system was fully implemented and was demonstrated to load trucks as fast as human operators.
Current-doubler based multiport DC/DC converter with galvanic isolation

This paper proposes a novel topology of a multiport DC/DC converter composed of an H-bridge inverter, a high-frequency galvanic isolation transformer, and a combined circuit with a current-doubler and a buck chopper. The topology has lower conduction loss by multiple current paths and smaller output capacitors by means of an interleave operation. Results of computer simulations and experimental tests show proper operations and feasibility of the proposed strategy.
Moon slotted circular planar monopole UWB antenna design and analysis

Design and analysis of a moon slotted circular planar monopole UWB (ultra-wide band) antenna is discussed. The proposed antenna operates within a frequency spectrum of 2.62GHz to 12.63GHz, which includes the unlicensed band 3.1GHz-10.6GHz approved by FCC in 2002. It achieves a maximal peak gain of 4.85dBi at 9.75GHz and highest radiation efficiency of 95.94% at 3.1GHz. This antenna is mounted on a FR 4 substrate having dimensions 30mm×19mm×1.6mm. This paper initially reports a systematic way of conventional circular planar monopole UWB antenna design and then presents a step-by-step design process to obtain the proposed UWB antenna from the conventional one. In this process transmission line equivalent models for different design steps are also discussed. These models are basically derived from the conventional assumption of copper elements interms of R, L and C. Then after, it reports a comparison study of the two UWB antennas. Finally, the proposed UWB antenna geometry is fabricated and tested. Experimental result shows an agreement with the simulated result.
A Novel Wake-Up Receiver with Addressing Capability for Wireless Sensor Nodes

Emerging low-power radio triggering techniques for wireless motes are a promising approach to prolong the lifetime of Wireless Sensor Networks (WSNs). By allowing nodes to activate their main transceiver only when data need to be transmitted or received, wake-up-enabled solutions virtually eliminate the need for idle listening, thus drastically reducing the energy toll of communication. In this paper we describe the design of a novel wake-up receiver architecture based on an innovative pass-band filter bank with high selectivity capability. The proposed concept, demonstrated by a prototype implementation, combines both frequency-domain and time-domain addressing space to allow selective addressing of nodes. To take advantage of the functionalities of the proposed receiver, as well as of energy-harvesting capabilities modern sensor nodes are equipped with, we present a novel wake-up-enabled harvesting-aware communication stack that supports both interest dissemination and converge casting primitives. This stack builds on the ability of the proposed WuR to support dynamic address assignment, which is exploited to optimize system performance. Comparison against traditional WSN protocols shows that the proposed concept allows to optimize performance tradeoffs with respect to existing low-power communication stacks.
A case of onychomatricoma: Classic clinical, dermoscopic, and nail-clipping histologic findings.

the Department of Dermatology, Thomas Jefferson Univerty Hospital. ication of this article was supported by 3Gen Inc. ing sources: None. licts of interest: None declared. int requests: Jason B. Lee, MD, Department of Dermatology, omas Jefferson University Hospital, 833 Chestnut St, Suite 0, Philadelphia, PA 19107.  0190-9622/$36.00 a 2016 by the American Academy of Dermatology, Inc.
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. "L"-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works. Our network architecture is similar to that of RoomNet , but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.
SceneGrok: inferring action maps in 3D environments

With modern computer graphics, we can generate enormous amounts of 3D scene data. It is now possible to capture high-quality 3D representations of large real-world environments. Large shape and scene databases, such as the Trimble 3D Warehouse, are publicly accessible and constantly growing. Unfortunately, while a great amount of 3D content exists, most of it is detached from the semantics and functionality of the objects it represents. In this paper, we present a method to establish a correlation between the geometry and the functionality of 3D environments. Using RGB-D sensors, we capture dense 3D reconstructions of real-world scenes, and observe and track people as they interact with the environment. With these observations, we train a classifier which can transfer interaction knowledge to unobserved 3D scenes. We predict a likelihood of a given action taking place over all locations in a 3D environment and refer to this representation as an action map over the scene. We demonstrate prediction of action maps in both 3D scans and virtual scenes. We evaluate our predictions against ground truth annotations by people, and present an approach for characterizing 3D scenes by functional similarity using action maps.
The roles of psychological climate, information management capabilities, and IT support on knowledge-sharing: an MOA perspective

02 March 2012 Revised: 08 May 2012 2nd  17 February 2013 3rd Revision: 08 May 2013 4th  25 June 2013  Leveraging the motivation-opportunity-ability (MOA) theoretical framework and past research on psychological climate, this study analyzes three antecedent factors driving an individual’s knowledge-sharing (KS) within organizations: knowledge-sharing psychological climate as motivation, information management capability as ability, and organizational information technology support as opportunity. An empirical examination reveals that a motivating psychological climate has a primary impact on KS behavior, and the impact of perceived information management capability on sharing is mediated by the psychological climate. Perceived organizational use of information technology to support knowledge work bears strong influence on information management capabilities but not on sharing, suggesting that investment in IT does have indirect payoffs. The study is the first to position the opportunity→ability→motivation causal network in an individual’s KS behavior. The findings suggest that managers need to consider the pre-requisite roles of IT-enabled opportunities and workers’ information management abilities when building an all-important motivating climate to share.  (2015) 24(1), 59–75. doi:10.1057/ejis.2013.25; published online 8 October 2013
Domain Classification of Textual Conversation Using Machine Learning Approach

This paper presents an approach for classification of textual conversation into multiple domain categories using support vector classifier. The feature reduction is done through Principal Component Analysis (PCA) to extract the important features from the feature vector. These features are passed to different configurations of SVM and the best one is chosen for the final process of classification. The domain's categories are defined on real life situations and conversation to train the system like education & research, personal, patriotism, terrorism, medical, religious, sports, and business. The experiment results show that the proposed method works effectively with more than 75% accuracy.
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning algorithms are available that make a bounded number of mistakes, with the bound independent of the number of examples seen by the learner. We present one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions. The basic method can be expressed as a linear-threshold algorithm. A primary advantage of this algorithm is that the number of mistakes grows only logarithmically with the number of irrelevant attributes in the examples. At the same time, the algorithm is computationally efficient in both time and space.
Revisiting Reuse for Approximate Query Processing

Visual data exploration tools allow users to quickly gather insights from new datasets. As dataset sizes continue to increase, though, new techniques will be necessary to maintain the interactivity guarantees that these tools require. Approximate query processing (AQP) attempts to tackle this problem and allows systems to return query results at “human speed.” However, existing AQP techniques start to break down when confronted with ad hoc queries that target the tails of the distribution. We therefore present an AQP formulation that can provide low-error approximate results at interactive speeds, even for queries over rare subpopulations. In particular, our formulation treats query results as random variables in order to leverage the ample opportunities for result reuse inherent in interactive data exploration. As part of our approach, we apply a variety of optimization techniques that are based on probability theory, including new query rewrite rules and index structures. We implemented these techniques in a prototype system and show that they can achieve interactivity where alternative approaches cannot.
WebSAIL wikifier at ERD 2014

In this paper, we report on our participation in Entity Recognition and Disambiguation Challenge 2014. We present WebSAIL Wikifier, an entity recognition and disambiguation system that identifies and links textual mentions to their referent entities in Wikipedia and later maps to the ERD set of target entities. WebSAIL Wikifier can handle both short text and long text queries. The system uses hybrid rule-based NER to discover mentions and a supervised machine learning approach to disambiguate their entities. The system achieves F1 of 0.641 and 0.687 on short track and long track respectively.
Fibrillar collagen scoring by second harmonic microscopy: a new tool in the assessment of liver fibrosis.

Imaging of supramolecular structures by multiphoton microscopy offers significant advantages for studying specific fibrillar compounds in biological tissues. In this study, we aimed to demonstrate the relevance of Second Harmonic Generation (SHG) for assessing and quantifying, without staining, fibrillar collagen in liver fibrosis.



We first showed the relationship between SHG signal and collagen forms over-produced and accumulated during fibrosis progression. Taking this property into consideration, we developed an innovative method to precisely quantify the fibrosis area in histological slices by scoring of fibrillar collagen deposits (Fibrosis-SHG index).



The scoring method was routinely applied to 119 biopsies from patients with chronic liver disease allowing a fast and accurate measurement of fibrosis correlated with the Fibrosis-Metavir score (rho=0.75, p<0.0001). The technique allowed discriminating patients with advanced (moderate to severe) fibrosis (AUROC=0.88, p<0.0001) and cirrhosis (AUROC=0.89, p<0.0001). Taking advantage of its continuous gradation, the Fibrosis-SHG index also allowed the discrimination of several levels of fibrosis within the same F-Metavir stage. The SHG process presented several advantages such as a high reliability and sensitivity that lead to a standardized evaluation of hepatic fibrosis in liver biopsies without staining and pathological examination.



Second harmonic microscopy emerges as an original and powerful tool in the assessment of liver fibrosis and offers new possibilities for the evaluation of experimental protocols. We expect that this technology could easily be applicable in the study of other fibro-proliferative pathologies.
The Penn Treebank: An Overview

The Penn Treebank, in its eight years of operation (1989-1996), produced approximately 7 million words of part-of-speech tagged text, 3 million words of skeletally parsed text, over 2 million words of text parsed for predicateargument structure, and 1.6 million words of transcribed spoken text annotated for speech disfluencies. This paper describes the design of the three annotation schemes used by the Treebank: POS tagging, syntactic bracketing, and disfluency annotation and the methodology employed in production.
Healing at implants installed concurrently to maxillary sinus floor elevation with Bio-Oss® or autologous bone grafts. A histo-morphometric study in rabbits.

To compare the healing at elevated sinus floors augmented either with deproteinized bovine bone mineral (DBBM) or autologous bone grafts and followed by immediate implant installation.



Twelve albino New Zealand rabbits were used. Incisions were performed along the midline of the nasal dorsum. The nasal bone was exposed. A circular bony widow with a diameter of 3 mm was prepared bilaterally, and the sinus mucosa was detached. Autologous bone (AB) grafts were collected from the tibia. Similar amounts of AB or DBBM granules were placed below the sinus mucosa. An implant with a moderately rough surface was installed into the elevated sinus bilaterally. The animals were sacrificed after 7 (n = 6) or 40 days (n = 6).



The dimensions of the elevated sinus space at the DBBM sites were maintained, while at the AB sites, a loss of 2/3 was observed between 7 and 40 days of healing. The implants showed similar degrees of osseointegration after 7 (7.1 ± 1.7%; 9.9 ± 4.5%) and 40 days (37.8 ± 15%; 36.0 ± 11.4%) at the DBBM and AB sites, respectively. Similar amounts of newly formed mineralized bone were found in the elevated space after 7 days at the DBBM (7.8 ± 6.6%) and AB (7.2 ± 6.0%) sites while, after 40 days, a higher percentage of bone was found at AB (56.7 ± 8.8%) compared to DBBM (40.3 ± 7.5%) sites.



Both Bio-Oss® granules and autologous bone grafts contributed to the healing at implants installed immediately in elevated sinus sites in rabbits. Bio-Oss® maintained the dimensions, while autologous bone sites lost 2/3 of the volume between the two periods of observation.
Transaction processing on confidential data using cipherbase

Cipherbase is a comprehensive database system that provides strong end-to-end data confidentiality through encryption. Cipherbase is based on a novel architecture that combines an industrial strength database engine (SQL Server) with lightweight processing over encrypted data that is performed in secure hardware. The overall architecture provides significant benefits over the state-of-the-art in terms of security, performance, and functionality. This paper presents a prototype of Cipherbase that uses FPGAs to provide secure processing and describes the system engineering details implemented to achieve competitive performance for transactional workloads. This includes hardware-software co-design issues (e.g. how to best offer parallelism), optimizations to hide the latency between the secure hardware and the main system, and techniques to cope with space inefficiencies. All these optimizations were carefully designed not to affect end-to-end data confidentiality. Our experiments with the TPC-C benchmark show that in the worst case when all data are strongly encrypted, Cipherbase achieves 40% of the throughput of plaintext SQL Server. In more realistic cases, if only critical data such as customer names are encrypted, the Cipherbase throughput is more than 90% of plaintext SQL Server.
The design of an output data collection framework for NS-3

An important design decision in the construction of a simulator is how to enable users to access the data generated in each run of a simulation experiment. As the simulator executes, the samples of performance metrics that are generated beg to be exposed either in their raw state or after having undergone mathematical processing. Also of concern is the particular format this data assumes when externalized to mass storage, since it determines the ease of processing by other applications or interpretation by the user. In this paper, we present a framework for the ns-3 network simulator for capturing data from inside an experiment, subjecting it to mathematical transformations, and ultimately marshaling it into various output formats. The application of this functionality is illustrated and analyzed via a study of common use cases. Although the implementation of our approach is specific to ns-3, this design presents lessons transferrable to other platforms.
Formative Evaluation of Business Model Representations - the Service Business Model Canvas

Drivers like global competition, advances in technology, and new attractive market opportunities foster a process of servitization and thus the search for innovative service business models. To facilitate this process, different methods and tools for the development of new business models have emerged. Nevertheless, business model approaches are missing that enable the representation of cocreation as one of the most important service-characteristics. Rooted in a cumulative research design that seeks to advance extant business model representations, this goal is to be closed by the Service Business Model Canvas (SBMC). This contribution comprises the application of thinking-aloud protocols for the formative evaluation of the SBMC. With help of industry experts and academics with experience in the service sector and business models, the usability is tested and implications for its further development derived. Furthermore, this study provides empirically based insights for the design of service business model representation that can facilitate the development of future business models.
Counterfactual thinking and emotions: regret and envy learning.

Emotions like regret and envy share a common origin: they are motivated by the counterfactual thinking of what would have happened had we made a different choice. When we contemplate the outcome of a choice we made, we may use the information on the outcome of a choice we did not make. Regret is the purely private comparison between two choices that we could have taken, envy adds to this the information on outcome of choices of others. However, envy has a distinct social component, in that it adds the change in the social ranking that follows a difference in the outcomes. We study the theoretical foundation and the experimental test of this view.
Combining Shape-Changing Interfaces and Spatial Augmented Reality Enables Extended Object Appearance

We propose combining shape-changing interfaces and spatial augmented reality for extending the space of appearances and interactions of actuated interfaces. While shape-changing interfaces can dynamically alter the physical appearance of objects, the integration of spatial augmented reality additionally allows for dynamically changing objects' optical appearance with high detail. This way, devices can render currently challenging features such as high frequency texture or fast motion. We frame this combination in the context of computer graphics with analogies to established techniques for increasing the realism of 3D objects such as bump mapping. This extensible framework helps us identify challenges of the two techniques and benefits of their combination. We utilize our prototype shape-changing device enriched with spatial augmented reality through projection mapping to demonstrate the concept. We present a novel mechanical distance-fields algorithm for real-time fitting of mechanically constrained shape-changing devices to arbitrary 3D graphics. Furthermore, we present a technique for increasing effective screen real estate for spatial augmented reality through view-dependent shape change.
Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices’ capacity. What is worse, app service providers need to collect and utilize a large volume of users’ data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9.83, 10−6)-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0.97% accuracy loss.
Parenting in Battered Women: The Effects of Domestic Violence on Women and Their Children

This study integrates an ecological perspective and trauma theory in proposing a model of the effects of domestic violence on women's parenting and children's adjustment. One hundred and twenty women and their children between the ages of 7 and 12 participated. Results supported an ecological model of the impact of domestic violence on women and children. The model predicted 40% of the variance in children's adjustment, 8% of parenting style, 43% of maternal psychological functioning, and 23% of marital satisfaction, using environmental factors such as social support, negative life events, and maternal history of child abuse. Overall, results support the ecological framework and trauma theory in understanding the effects of domestic violence on women and children. Rather than focusing on internal pathology, behavior is seen to exist on a continuum influenced heavily by the context in which the person is developing.