nils-herrmann/clean_scirepeval huggingface dataset:
Domain Objects and Microservices for Systems Development: A Roadmap

This paper discusses a roadmap to investigate Domain Objects being an adequate formalism to capture the peculiarity of microservice architecture, and to support Software development since the early stages. It provides a survey of both Microservices and Domain Objects, and it discusses plans and reflections on how to investigate whether a modeling approach suited to adaptable service-based components can also be applied with success to the microservice scenario.
Timbre-specific enhancement of auditory cortical representations in musicians.

Neural imaging studies have shown that the brains of skilled musicians respond differently to musical stimuli than do the brains of non-musicians, particularly for musicians who commenced practice at an early age. Whether brain attributes related to musical skill are attributable to musical practice or are hereditary traits that influence the decision to train musically is a subject of controversy, owing to its pedagogic implications. Here we report that auditory cortical representations measured neuromagnetically for tones of different timbre (violin and trumpet) are enhanced compared to sine tones in violinists and trumpeters, preferentially for timbres of the instrument of training. Timbre specificity is predicted by a principle of use-dependent plasticity and imposes new requirements on nativistic accounts of brain attributes associated with musical skill.
Resistance to forgetting associated with hippocampus-mediated reactivation during new learning

One of the reasons why we forget past experiences is because we acquire new memories in the interim. Although the hippocampus is thought to be important for acquiring and retaining memories, there is little evidence linking neural operations during new learning to the forgetting (or remembering) of earlier events. We found that, during the encoding of new memories, responses in the human hippocampus are predictive of the retention of memories for previously experienced, overlapping events. This brain-behavior relationship is evident in neural responses to individual events and in differences across individuals. We found that the hippocampus accomplishes this function by reactivating older memories as new memories are formed; in this case, reactivating neural responses that represented monetary rewards associated with older memories. These data reveal a fundamental mechanism by which the hippocampus tempers the forgetting of older memories as newer memories are acquired.
PIRAT - A System for Quantitative Sewer Pipe Assessment

Sewers are aging, expensive assets that attract public attention only when they fail. Sewer operators are under increasing pressure to minimise their maintenance costs, while preventing sewer failures. Inspection can give early warning of failures and allow economical repair under noncrisis conditions. Current inspection techniques are subjective and detect only gross defects reliably. They cannot provide the data needed to confidently plan long-term maintenance. This paper describes PIRAT, a quantitative technique for sewer inspection. PIRAT measures the internal geometry of the sewer and then analyses these data to detect, classify, and rate defects automatically using artificial intelligence techniques. We describe the measuring system and present and discuss geometry results for different types of sewers. The defect analysis techniques are outlined and a sample defect report presented. PIRAT’s defect reports are compared with reports from the conventional technique and the discrepancies discussed. We relate PIRAT to other work in sewer robotics. sewer inspection robot, sewer condition assessment, neural network
Exposing digital forgeries in video by detecting double MPEG compression

With the advent of sophisticated and low-cost video editing software, it is becoming increasingly easier to tamper with digital video. In addition,an ever-growing number of video surveillance cameras is giving rise to an enormous amount of video data. The ability to ensure the integrity and authenticity of this data poses considerable challenges. Here we begin to explore techniques for detecting traces of tampering in digital video. Specifically, we show how a doublycompressed MPEG video sequence introduces specific static and temporal statistical perturbations whose presence can be used as evidence of tampering.
Jointly Deep Multi-View Learning for Clustering Analysis.

In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignments can be learned simultaneously. Our key idea is that the joint learning strategy can sufficiently exploit clustering-friendly multi-view features and useful multi-view complementary information to improve the clustering performance. How to realize the multi-view fusion in such a joint framework is the primary challenge. To do so, we design two ingenious variants of deep multi-view joint clustering models under the proposed framework, where multi-view fusion is implemented by two different schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence like clustering objective. Experiments on six challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multiview clustering methods, which proves the effectiveness of the proposed DMJC framework. To our best knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.
Xdroid: Testing Android Apps with Dependency Injection

The applications ("apps") running on Android need to be adequately tested to avoid faults. Researchers have developed a number of test input generation tools for automated app testing and tried to improve test coverage to detect as many faults as possible. However, existing testing tools achieve very low coverage for some specific apps because they highly depend on external factors to run properly such as business logic, content providers and so on. In this paper, we present Xdroid to catch when and what kind of dependencies apps require and inject them correspondingly in a lightweight way. Working with a built-in tool Xmonkey which generates GUI events directly on Android devices, Xdroid implements an effective testing engine to get a high coverage. We evaluate Xdroid with diverse Android apps and demonstrate that it outperforms Monkey for 17%, Sapienz for 22% in coverage and meanwhile reveals more bugs than manual testing. Overall, it combines the benefits of both manual testing and random testing to improve test coverage and detect bugs effectively.
Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm

In order to optimize the resource utilization of physical machines (PMs), the workload prediction of virtual machines (VMs) is vital but challenging. Most of existing literatures focus on either resource prediction or allocation individually, but both of them are highly correlated. In this paper, we propose a multiobjective genetic algorithm (GA) to dynamically forecast the resource utilization and energy consumption in cloud data center. We formulate a multiobjective optimization problem of resource allocation, which considers the CPU and memory utilization of VMs and PMs, and the energy consumption of data center. The proposed GA forecasts the resource requirement of next time slot according to the historical data in previous time slots. We further propose a VM placement algorithm to allocate VMs for next time slot based on the prediction results of GA. In our simulation-based analysis, the optimal solution for resource prediction under stable and unstable utilization tendency is found by the proposed GA. The prediction result is superior to the previous proposed Grey forecasting model. Results show that the proposed VM placement algorithm not only increases the average utilization level of CPU and memory but also decreases the energy consumption of cloud data center.
Wideband Branch-Line Couplers With Single-Section Quarter-Wave Transformers for Arbitrary Coupling Levels

Wideband branch-line couplers with arbitrary coupling levels are demonstrated. By integrating single-section quarter-wave transformers at each port, wideband characteristic with excellent coupling flatness is achieved. Also, a method is demonstrated that introduces intentional mismatch for a further enhancement in bandwidth. Superior structural and design simplicity outperform previous wideband couplers. Branch-line couplers at 3 GHz are demonstrated experimentally that maintain the coupling level within 0.5 dB from the specified 10 dB in as much as a 50.9% bandwidth, with minimum return loss and isolation of 16.3 and 18.7 dB, respectively.
The Impact of Training and Social Norms on Information Security Compliance: A Pilot Study

Security training has been shown to be an important factor that impacts employees’ intentions to comply with organization’s security policies. In this study, we define and then study the impact of two sub-constructs of security training, threat appraisal and policy awareness, on intentions to comply with organizational security policies. Injunctive and descriptive norms, which are standards of behavior that recommends and forbids behavior in specific circumstances, have been hypothesized as mediators between training constructs and behavioral intention to comply. We pilot-tested our proposed set of hypotheses with survey data collected from 69 employees in a higher education institute. Results supported our proposed model. Based on the findings, implications for theory and practices are discussed.
A Safe, Efficient Algorithm for Regression Test Selection

Regression testing is a necessary but costly maintenance activity aimed at demonstrating that code has not been adversely aaected by changes. A selective approach to regression testing selects tests for a modi-ed program from an existing test suite. We present a new technique for selective regression testing. Our algorithm constructs control dependence graphs for program versions, and uses these graphs to determine which tests from the existing test suite may exhibit changed behavior on the new version. Unlike most previous techniques for selective retest, our algorithm selects every test from the original test suite that might expose errors in the modiied program, and does this without prior knowledge of program modiications. Our algorithm handles all language constructs and program modiications, and is easily automated.
Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

The aim of this study was to use a three-dimensional (3D) visualization technology to illustrate and describe the anatomical features of the penile suspensory ligamentous system based on the Visible Human data sets and to explore the suspensory mechanism of the penis for the further improvement of the penis-lengthening surgery.  data sets were used to segment the suspensory ligamentous system and its adjacent structures. The magnetic resonance imaging (MRI) images of this system were studied and compared with those from the Visible Human data sets. The 3D models reconstructed from the Visible Human data sets were used to provide morphological features of the penile suspensory ligamentous system and its related structures.  The fundiform ligament was a superficial, loose, fibro-fatty tissue which originated from Scarpa's fascia superiorly and continued to the scrotal septum inferiorly. The suspensory ligament and arcuate pubic ligament were dense fibrous connective tissues which started from the pubic symphysis and terminated by attaching to the tunica albuginea of the corpora cavernosa. Furthermore, the arcuate pubic ligament attached to the inferior rami of the pubis laterally.  The 3D model based on Visible Human data sets can be used to clarify the anatomical features of the suspensory ligamentous system, thereby contributing to the improvement of penis-lengthening surgery.
Towards value-based pricing — An integrative framework for decision making

Despite a recent surge of interest, the subject of pricing in general and value-based pricing in particular has received little academic investigation. Yet, pricing has a huge impact on financial results, both in absolute terms and relative to other instruments of the marketing mix. The objective of this paper is to present a comprehensive framework for pricing decisions which considers all relevant dimensions and elements for profitable and sustainable pricing decisions. The theoretical framework is useful for guiding new product pricing decisions as well as for implementing price-repositioning strategies for existing products. The practical application of this framework is illustrated by a case study involving the pricing decision for a major product launch at a global chemical company. D 2003 Elsevier Inc. All rights reserved.
Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments

This paper tackles a privacy breach in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. For example, a user who wants to issue a query asking about her nearest gas station has to report her exact location to an LBS provider. However, many recent research efforts have indicated that revealing private location information to potentially untrusted LBS providers may lead to major privacy breaches. To preserve user location privacy, spatial cloaking is the most commonly used privacy-enhancing technique in LBS. The basic idea of the spatial cloaking technique is to blur a user’s exact location into a cloaked area that satisfies the user specified privacy requirements. Unfortunately, existing spatial cloaking algorithms designed for LBS rely on fixed communication infrastructure, e.g., base stations, and centralized/distributed servers. Thus, these algorithms cannot be applied to a mobile peer-to-peer (P2P) environment where mobile users can only communicate with other peers through P2P multi-hop routing without any support of fixed communication infrastructure or servers. In this paper, we propose a spatial cloaking algorithm for mobile P2P environments. As mobile P2P environments have many unique limitations, e.g., user mobility, limited transmission range, multi-hop communication, scarce commuThis work is supported in part by the National Science Foundation under Grant Numbers IIS-0811998, IIS-0811935, and CNS-0708604, and by Microsoft Research Gift. Chi-Yin Chow University of Minnesota Twin Cities Department of Computer Science and Engineering 200 Union Street SE, Minneapolis, MN 55455, USA E-mail: cchow@cs.umn.edu Mohamed F. Mokbel University of Minnesota Twin Cities Department of Computer Science and Engineering 200 Union Street SE, Minneapolis, MN 55455, USA E-mail: mokbel@cs.umn.edu Xuan Liu IBM Thomas J. Watson Research Center 19 Skyline Drive, Hawthorne, NY 10532, USA E-mail: xuanliu@us.ibm.com
Treatment of thoracolumbar burst fractures without neurologic deficit by indirect reduction and posterior instrumentation: bisegmental stabilization with monosegmental fusion

This study retrospectively reviews 20 sequential patients with thoracolumbar burst fractures without neurologic deficit. All patients were treated by indirect reduction, bisegmental posterior transpedicular instrumentation and monosegmental fusion. Clinical and radiological outcome was analyzed after an average follow-up of 6.4 years. Re-kyphosis of the entire segment including the cephaled disc was significant with loss of the entire postoperative correction over time. This did not influence the generally benign clinical outcome. Compared to its normal height the fused cephalad disc was reduced by 70% and the temporarily spanned caudal disc by 40%. Motion at the temporarily spanned segment could be detected in 11 patients at follow-up, with no relation to the clinical result. Posterior instrumentation of thoracolumbar burst fractures can initially reduce the segmental kyphosis completely. The loss of correction within the fractured vertebral body is small. However, disc space collapse leads to eventual complete loss of segmental reduction. Therefore, posterolateral fusion alone does not prevent disc space collapse. Nevertheless, clinical long-term results are favorable. However, if disc space collapse has to prevented, an interbody disc clearance and fusion is recommended.
Instantaneous ego-motion estimation using multiple Doppler radars

The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.
Design of a 100 W, 500000 rpm permanent-magnet generator for mesoscale gas turbines

Mesoscale gas turbine generator systems are a promising solution for high energy and power density portable devices. This paper focuses on the design of a 100 W, 500000 rpm generator suitable for use with a gas turbine. The design procedure selects the suitable machine type and bearing technology, and determines the electromagnetic characteristics. The losses caused by the high frequency operation are minimized by optimizing the winding and the stator core material. The final design is a permanent-magnet machine with a volume of 3.5 cm/sup 3/ and experimental measurements from a test bench are presented.
Dual-Band Circularly Polarized Rotated Patch Antenna With a Parasitic Circular Patch Loading

A dual-band circularly polarized (CP) rotated patch antenna is proposed in this letter. Dual-band CP operation of the proposed antenna is achieved by adopting a circular patch below the rotated rectangular patch. The extra circular patch not only provides a higher band, but is also employed to tune the operating frequency of the lower band, which is generated by the rotated rectangular patch. The rectangular patch and the circular patch can be arranged on the front and back of a thin substrate and fed by a single slot. The dual CP operating bands can be tuned separately with a small frequency ratio of 1.18. A prototype of the proposed antenna is built to validate the design strategy. The measured 3-dB axial-ratio (AR) bandwidth is 2.1% (2.238-2.285 GHz) for the lower band and 2.0% (2.645-2.695 GHz) for the higher band, with the reflection coefficient being lower than -10 dB. The radiation pattern and gain are also measured and compared to the simulated ones.
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras

Understanding the surrounding environment of the vehicle is still one of the challenges for autonomous driving. This paper addresses 360-degree road scene semantic segmentation using surround view cameras, which are widely equipped in existing production cars. First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built. The model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images. Experiments demonstrate the effectiveness of the RDC to handle images with large distortions, and the proposed approach shows a good performance using surround view cameras with the help of the transformed images.
Agents Teaching Humans in Reinforcement Learning Tasks

This paper extends our existing teacher-student framework to allow a knowledgeable agent to teach human students. An agent teacher instructs a human student by suggesting actions the student should take as it learns. This paper extends previous algorithms, used for agents teaching other agents, to develop several new algorithms for agents teaching humans. Our results in the Pac-Man domain show that our new approaches can indeed be effectively used to improve human learning. Moreover, some of these human-teaching approaches perform better than some of the original algorithms when one agent teaches another agent.
Applying Deep Learning to the Newsvendor Problem

The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. If the probability distribution of the demand is known, the problem can be solved analytically. However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may be not optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately, as is typically done, and does not require knowledge of the probability distributions of the demand. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r,Q) policies.
Circularly Polarized Stacked Annular-Ring Microstrip Antenna With Integrated Feeding Network for UHF RFID Readers

A novel circularly polarized (CP) annular-ring microstrip antenna (ARMSA) is proposed for American ultrahigh frequency (UHF) radio frequency identification (RFID) readers. The annular ring is fed by an equal-split power divider with 90° phase difference. For the compactness of the entire antenna, the divider with the shape of Archimedes gradual-change lines is designed at the inside of the annular ring. A circular parasitic patch is suspended above the ring to improve the impedance matching and bandwidth. It also enhances the performances of axial ratio (AR). The impedance bandwidth for S11 <; -10 dB and 3-dB AR bandwidth are 870-967 MHz (10.6% at 915 MHz) and 893-948 MHz (6%), respectively. The CP gain is 8.9 dBi at 915 MHz with the front-to-back ratio (F/B) of 16.5 dB. The proposed antenna is low profile as the total height is 0.043 λ0 (λ0 is the free-space wavelength).