nils-herrmann/clean_scirepeval huggingface dataset:
Ship motion sensor isolation system development and testing for use with low cost IMUs

A sensor isolation system was developed to reduce vibrational and noise effects on MEMS IMU sensors. A single degree of freedom model of an isolator was developed and simulated. Then a prototype was constructed for use with a Microstrain 3DM-GX3-25 IMU sensor and experimentally tested on a six DOF motion platform. An order of magnitude noise reduction was observed on the z accelerometer up to seven Hz. The isolator was then deployed on a naval ship along with a DMS TSS-25 IMU used as a truth measurement and a rigid mounted 3DM sensor was used for comparison. Signal quality improvements of the IMU were characterized and engine noise at 20 Hz was reduced by tenfold on x, y, and z accelerometers. A heave estimation algorithm was implemented and several types of filters were evaluated. Lab testing with a six DOF motion platform with pure sinusoidal motion, a fixed frequency four pole bandpass filter provided the least heave error at 12.5% of full scale or 0.008m error. When the experimental sea data was analyzed a fixed three pole highpass filter yielded the most accurate results of the filters tested. A heave period estimator was developed to adjust the filter cutoff frequencies for varying sea conditions. Since the ship motions were small, the errors w.r.t. full scale were rather large at 78% RMS as a worst case and 44% for a best case. In absolute terms when the variable filters and isolator were implemented, the best case peak and RMS errors were 0.015m and 0.050m respectively. The isolator improves the heave accuracy by 200% to 570% when compared with a rigidly mounted 3DM sensor.
A phase-adjustable Delay-Locked Loop utilizing embedded phase interpolation

This paper presents the design of a 1 GHz Delay-Locked Loop (DLL) with embedded phase interpolation. The DLL was designed in a 0.13µm SiGe BiCMOS process and provides a measured single-tap phase range of 50ps (18°) with an average and worst-case phase resolution of 1.36ps (0.49°) and 5ps (1.8°), respectively. This translates to beamsteering resolutions of &#60; 3.5mm in free space when integrated into mm-wave imagers that utilize time-domain beamforming. The DLL has an average output rms-jitter (across all interpolation steps) of 860fs. The DLL consumes 33 mW of power and occupies an area of 0.185mm2.
CroRank: Cross Domain Personalized Transfer Ranking for Collaborative Filtering

Collaborative filtering techniques aim at recommending products to users based on their historical feedback. And many algorithms focus on personalized ranking problem with implicit feedback due to the "one-class" nature of many real-world datasets in a variety of services. Most of the existing personalized ranking methods are confined to one domain of data source and the question of how to model users' preferences information across distinct domains is usually be ignored. There are some transfer learning approaches that try to transfer numerical ratings, auxiliary social relations and other information across different domains but they do not address how users' preferences information varies from one domain to another accordingly. And they mainly exploit rating prediction problem rather than personalized ranking problem. In this paper, we propose an algorithm called CroRank to address the question, "How to bridge users' preferences information across different domains to promote better personalized ranking performance?". There are two main steps in CroRank, we first present an algorithm called multiple binomial matrix factorization (MBMF) to bridge the gap between items from distinct sources and then we introduce transfer Bayesian personalized ranking (TBPR) to recommend items for each user in the target domain. In CroRank, users' inclinations can transfer from the auxiliary domain to the target domain to provide better personalized ranking results. We compare CroRank to the state-of-the-art non-transfer models to demonstrate the improvements in flexibility and effectiveness.
An Adaptive Relaying Scheme for Fuse Saving in Distribution Networks With Distributed Generation

In some situations, utilities may try to “save” the fuse of a circuit following temporary faults by de-energizing the line with the fast operation of an upstream recloser before the fuse is damaged. This fuse-saving practice is accomplished through proper time coordination between a recloser and a fuse. However, the installation of distributed generation (DG) into distribution networks may affect this coordination due to additional fault current contributions from the distributed resources. This phenomenon of recloser-fuse miscoordination is investigated in this paper with the help of a typical network that employs fuse saving. The limitations of a recloser equipped with time and instantaneous overcurrent elements with respect to fuse savings, in the presence of DG, are discussed. An adaptive relaying strategy is proposed to ensure fuse savings in the new scenario even in the worst fault conditions. The simulation results obtained by adaptively changing relay settings in response to changing DG configurations confirm that the settings selected theoretically in accordance with the proposed strategy hold well in operation.
Computing the Ramsey number R(4,3,3) using abstraction and symmetry breaking

The number R(4, 3, 3) is often presented as the unknown Ramsey number with the best chances of being found “soon”. Yet, its precise value has remained unknown for almost 50 years. This paper presents a methodology based on abstraction and symmetry breaking that applies to solve hard graph edge-coloring problems. The utility of this methodology is demonstrated by using it to compute the value R(4, 3, 3) = 30. Along the way it is required to first compute the previously unknown set ℛ ( 3 , 3 , 3 ; 13 ) $\mathcal {R}(3,3,3;13)$ consisting of 78,892 Ramsey colorings.
Enzyme Assay Guided Isolation of an α-Amylase Inhibitor Flavonoid from Vaccinium arctostaphylos Leaves

The management of postprandial hyperglycemia is an important strategy in the control of diabetes mellitus and complications associated with the disease, especially in the diabetes type 2. Therefore, inhibitors of carbohydrate hydrolyzing enzymes can be useful in the treatment of diabetes and medicinal plants can offer an attractive strategy for the purpose. Vaccinium arctostaphylos leaves are considered useful for the treatment of diabetes mellitus in some countries. In our research for antidiabetic compounds from natural sources, we found that the methanol extract of the leaves of V. arctostaphylos displayed a potent inhibitory activity on pancreatic α-amylase activity (IC50 = 0.53 (0.53 - 0.54) mg/mL). The bioassay-guided fractionation of the extract resulted in the isolation of quercetin as an active α-amylase inhibitor. Quercetin showed a dose-dependent inhibitory effect with IC50 value 0.17 (0.16 - 0.17) mM.
A Temporal Permission Analysis and Enforcement Framework for Android

Permission-induced attacks, i.e., security breaches enabled by permission misuse, are among the most critical and frequent issues threatening the security of Android devices. By ignoring the temporal aspects of an attack during the analysis and enforcement, the state-of-the-art approaches aimed at protecting the users against such attacks are prone to have low-coverage in detection and high-disruption in prevention of permission-induced attacks. To address this shortcomings, we present Terminator, a temporal permission analysis and enforcement framework for Android. Leveraging temporal logic model checking,Terminator's analyzer identifies permission-induced threats with respect to dynamic permission states of the apps. At runtime, Terminator's enforcer selectively leases (i.e., temporarily grants) permissions to apps when the system is in a safe state, and revokes the permissions when the system moves to an unsafe state realizing the identified threats. The results of our experiments, conducted over thousands of apps, indicate that Terminator is able to provide an effective, yet non-disruptive defense against permission-induced attacks. We also show that our approach, which does not require modification to the Android framework or apps' implementation logic, is highly reliable and widely applicable.
Environmental Equity: The Demographics of Dumping

Research addressing “environmental equity” and “environmental racism” claims that facilities for treatment, storage, and disposal of hazardous wastes (TSDFs) are located disproportionately in minority areas. In the first comprehensive study of TSDFs to use census tract-level data, we find no nationally consistent and statistically significant differences between the racial or ethnic composition of tracts which contain commercial TSDFs and those which do not. TSDFs are more likely to be found in tracts with Hispanic groups, primarily in regions with the greatest percentage of Hispanics. Different geographic units of analysis elaborate on, but are consistent with, these results.
Asphyxia: a rare cause of death for motor vehicle crash occupants.

Motor vehicle related trauma is one of the leading causes of traumatic death. Although most of these deaths are because of severe blunt force trauma, there are people without severe injury who die of asphyxia related to the motor vehicle collision. There were 37 deaths because of motor vehicle related asphyxia in San Diego County during 1995-2004. Almost half (48.6%) of these deaths were because of compression asphyxia, 29.7% were positional asphyxia deaths, and 16.2% died of a combination of compression and positional asphyxia. We were unable to classify the mechanism of asphyxia for the remaining 5.4% of asphyxia deaths. Almost all occupants dying from asphyxia were involved in rollover crashes and may have been incapacitated by obesity, drug or alcohol intoxication, or blunt force trauma. Compression asphyxia deaths occurred both from vehicle crush with intrusion into the passenger compartment and from ejection of the occupant and subsequent crushing by the vehicle. Positional asphyxia occurred in positions interfering with normal respiration, including inversion. None of the occupants had injury severe enough to result in death at the scene if they had not first died of asphyxia. This study suggests classifying the mechanism of asphyxia for these fatalities may be a challenge to forensic pathologists who seldom see these rare deaths.
Adaptive local contrast enhancement for the visualization of high dynamic range images

In this paper, we present a new tone mapping algorithm for the display of high dynamic range images, inspired by adaptive process of the human visual system. The proposed algorithm is based on the center-surround Retinex processing. In our method, the local details are enhanced according to a non-linear adaptive spatial filter (Gaussian filter), whose shape (filter variance) is adapted to high-contrast edges of the image. Thus our method does not generate halo artifacts meanwhile preserves visibility and contrast impression of high dynamic range scenes in the common display devices. The proposed method is tested on a variety of HDR images and the results show the good performance of our method in terms of visual quality.
Segmentation in thermography images for bearing defect analysis in induction motors

This paper proposes a methodology to be used in the segmentation of infrared thermography images for the detection of bearing faults in induction motors. The proposed methodology can be a helpful tool for preventive and predictive maintenance of the induction motor. This methodology is based on manual threshold image processing to obtain a segmentation of an infrared thermal image, which is used for the detection of critical points known as hot spots on the system under test. From these hot spots, the parameters of interest that describe the thermal behavior of the induction motor were obtained. With the segmented image, it is possible to compare and analyze the thermal conditions of the system.
An erythematous plaque on the glans penis of a 60-year-old man.

A 60-year-old man presented with a three-month history of a reddish patch with ulceration on his glans penis. The lesion was minimally pruritic with no history of bleeding on touch. The patient had been diagnosed with adenocarcinoma of the prostate two years ago for which he had undergone radical prostatectomy with radiation therapy. Clinical examination revealed an erythematous plaque, measuring about 2 · 1 cm on the right side of the glans penis . The plaque was well defined with an irregular border and an overlying area of ulceration. The inguinal lymph nodes were not palpable, and the rest of physical examination was normal. The chest x-ray and ultrasound of the abdomen were within normal limits. Routine investigations revealed normal blood counts, renal and liver function tests. Serum prostate-specific antigen levels were also within normal limits. A biopsy specimen of the lesion was obtained for histopathological examination (Figs. 2 and 3).
A global map of human impact on marine ecosystems.

The management and conservation of the world's oceans require synthesis of spatial data on the distribution and intensity of human activities and the overlap of their impacts on marine ecosystems. We developed an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems. Our analysis indicates that no area is unaffected by human influence and that a large fraction (41%) is strongly affected by multiple drivers. However, large areas of relatively little human impact remain, particularly near the poles. The analytical process and resulting maps provide flexible tools for regional and global efforts to allocate conservation resources; to implement ecosystem-based management; and to inform marine spatial planning, education, and basic research.
A self-organizing neural network architecture for learning human-object interactions

The visual recognition of transitive actions comprising human-object interactions is a key component for artificial systems operating in natural environments. This challenging task requires jointly the recognition of articulated body actions as well as the extraction of semantic elements from the scene such as the identity of the manipulated objects. In this paper, we present a self-organizing neural network for the recognition of human-object interactions from RGB-D videos. Our model consists of a hierarchy of GrowWhen-Required (GWR) networks that learn prototypical representations of body motion patterns and objects, accounting for the development of action-object mappings in an unsupervised fashion. We report experimental results on a dataset of daily activities collected for the purpose of this study as well as on a publicly available benchmark dataset. In line with neurophysiological studies, our self-organizing architecture exhibits higher neural activation for congruent action-object pairs learned during training sessions with respect to synthetically created incongruent ones. We show that our unsupervised model shows competitive classification results on the benchmark dataset with respect to strictly supervised approaches.
Grasping objects localized from uncertain point cloud data

Robotic grasping is very sensitive to how accurate is the pose estimation of the object to grasp. Even a small error in the estimated pose may cause the planned grasp to fail. Several methods for robust grasp planning exploit the object geometry or tactile sensor feedback. However, object pose range estimation introduces specific uncertainties that can also be exploited to choose more robust grasps. We present a grasp planning method that explicitly considers the uncertainties on the visually-estimated object pose. We assume a known shape (e.g. primitive shape or triangle mesh), observed as a –possibly sparse– point cloud. The measured points are usually not uniformly distributed over the surface as the object is seen from a particular viewpoint; additionally this non-uniformity can be the result of heterogeneous textures over the object surface, when using stereo-vision algorithms based on robust feature-point matching. Consequently the pose estimation may be more accurate in some directions and contain unavoidable ambiguities. The proposed grasp planner is based on a particle filter to estimate the object probability distribution as a discrete set. We show that, for grasping, some ambiguities are less unfavorable so the distribution can be used to select robust grasps. Some experiments are presented with the humanoid robot iCub and its stereo cameras.
Synthesizing Training Data for Object Detection in Indoor Scenes

Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-ofthe-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMUKitchens [5] and the Washington RGB-D Scenes v2 . As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.
Cross-Modal Transfer Learning for HEp-2 Cell Classification Based on Deep Residual Network

Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases. However, the traditional approach requires experienced experts to artificially identify cell patterns, which extremely increases the workload and suffer from the subjective opinion of physician. To address it, we propose a very deep residual network (ResNet) based framework to automatically recognize HEp-2 cell via cross-modal transfer learning strategy. We adopt a residual network of 50 layers (ResNet-50) that are substantially deep to acquire rich and discriminative feature. Compared with typical convolutional network, the main characteristic of residual network lie in the introduction of residual connection, which can solve the degradation problem effectively. Also, we use a cross-modal transfer learning strategy by pre-training the model from a very similar dataset (from ICPR2012 to ICPR2016-Task1). Our proposed framework achieves an average class accuracy of 95.63% on ICPR2012 HEp-2 dataset and a mean class accuracy of 96.87% on ICPR2016-Task1 HEp-2 dataset, which outperforms the traditional methods.
Terrain Response Estimation Using an Instrumented Rocker-Bogie Mobility System

This paper presents a procedure to model the drawbar pull and resistive torque of an unknown terrain as a function of normal load and slip using on-board rover instruments. Kapvik , which is a planetary micro-rover prototype with a rocker-bogie mobility system, is simulated in two dimensions. A suite of sensors is used to take relevant measurements; in addition to typical rover measurements, forces above the wheel hubs and rover forward velocity are sensed. An estimator determines the drawbar pull, resistive torque, normal load, and slip of the rover. The collected data are used to create a polynomial fit model that closely resembles the real terrain response.
Education and Economic Growth

This paper provides a survey of work on the link between education and economic growth. It shows that data from the early 20th century are coherent with conclusions about education and economic growth derived from the much more recent past. It also presents an analysis of the role of education in facilitating the use of best-practice technology. It is to be published in the International Handbook on the Economics of Education edited by G and J. Johnes and published by Edward Elgar.
Assessing the New Way of Working: Bricks, bytes and Behaviour

The world in which we work is changing. Information and communication technologies transform the work environment, providing the flexibility of when and where to work. The New Way of Working (NWOW) is a relatively new phenomenon that provides the context for these developments. It consists of three distinct pillars that are referred to as Bricks, Bytes and Behaviour. These pillars formed the basis for the development of the NWOW Analysis Monitor that enables organisations to determine their current level of NWOW adoption and provides guidance for future initiatives in adopting NWOW practices. The level of adoption is determined from both the manager’s and employees’ perspective as they might have a different perception and/or expectations regarding NWOW. The development of the multi-level NWOW Analysis Monitor is based on the Design Science Research approach. The monitor has been evaluated in two cases, forming two iterations in the design science research cycle. It has proved to be a useful assessment tool for organisations in the process of implementing NWOW. In future research the NWOW Analysis Monitor will be used in quantitative research on the effects of the implementation of NWOW on the organisation and its performance.
A systematic literature review on quality criteria for agile requirements specifications

The quality of requirements is typically considered as an important factor for the quality of the end product. For traditional up-front requirements specifications, a number of standards have been defined on what constitutes good quality : Requirements should be complete, unambiguous, specific, time-bounded, consistent, etc. For agile requirements specifications, no new standards have been defined yet, and it is not clear yet whether traditional quality criteria still apply. To investigate what quality criteria for assessing the correctness of written agile requirements exist, we have conducted a systematic literature review. The review resulted in a list of 16 selected papers on this topic. These selected papers describe 28 different quality criteria for agile requirements specifications. We categorize and analyze these criteria and compare them with those from traditional requirements engineering. We discuss findings from the 16 papers in the form of recommendations for practitioners on quality assessment of agile requirements. At the same time, we indicate the open points in the form of a research agenda for researchers working on this topic .