nils-herrmann/clean_scirepeval huggingface dataset:
Information systems in supply chain integration and management

Supply chain management (SCM) is the 21st century global operations strategy for achieving organizational competitiveness. Companies are attempting to find ways to improve their flexibility and responsiveness and in turn competitiveness by changing their operations strategy, methods and technologies that include the implementation of SCM paradigm and information technology (IT). However, a thorough and critical review of literature is yet to be carried out with the objective of bringing out pertinent factors and useful insights into the role and implications of IT in SCM. In this paper, the literature available on IT in SCM have been classified using suitable criteria and then critically reviewed to develop a framework for studying the applications of IT in SCM. Based on this review and analysis, recommendations have been made regarding the application of IT in SCM and some future research directions are indicated.
3-D Analytical Model of Helical Winding PM Machines Including Rotor Eddy Currents

Helical windings (or zigzag windings) are used in a number of applications, however, in electrical machines, mainly employed in low-power, high-speed permanent magnet (PM) brushless dc machines due to the cost effectiveness of the winding type while maintaining reasonable performance. Typically, helical windings are used for low-voltage applications due to their spiral form, which makes them most suitable for a small number of turns. In high-speed electrical machines, such a low number of turns are applicable. It is apparent that high-speed PM machines suffer from rotor eddy-current losses, which in some cases may lead to PM demagnetization due to overheating. The performance of the machine is compromised by these losses; hence, they have to be considered during the design procedure. There are many papers analyzing the magnetic field of these machines employing helical windings; however, none of them present a simple and precise electromagnetic model of a machine with the helical winding. This paper presents an analytical approach to model the resulting 3-D magnetic field of the helical winding, considering eddy currents in the conducting media of the rotor. The model is verified with the 3-D finite-element method by means of comparing magnetic field and rotor eddy-current losses.
Person-Independent 3D Gaze Estimation Using Face Frontalization

Person-independent and pose-invariant estimation of eye-gaze is important for situation analysis and for automated video annotation. We propose a fast cascade regression based method that first estimates the location of a dense set of markers and their visibility, then reconstructs face shape by fitting a part-based 3D model. Next, the reconstructed 3D shape is used to estimate a canonical view of the eyes for 3D gaze estimation. The model operates in a feature space that naturally encodes local ordinal properties of pixel intensities leading to photometric invariant estimation of gaze. To evaluate the algorithm in comparison with alternative approaches, three publicly-available databases were used, Boston University Head Tracking, Multi-View Gaze and CAVE Gaze datasets. Precision for head pose and gaze averaged 4 degrees or less for pitch, yaw, and roll. The algorithm outperformed alternative methods in both datasets.
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pascal 3D+ benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.
Example-based video color grading

In most professional cinema productions, the color palette of the movie is painstakingly adjusted by a team of skilled colorists -- through a process referred to as color grading -- to achieve a certain visual look. The time and expertise required to grade a video makes it difficult for amateurs to manipulate the colors of their own video clips. In this work, we present a method that allows a user to transfer the color palette of a model video clip to their own video sequence. We estimate a per-frame color transform that maps the color distributions in the input video sequence to that of the model video clip. Applying this transformation naively leads to artifacts such as bleeding and flickering. Instead, we propose a novel differential-geometry-based scheme that interpolates these transformations in a manner that minimizes their curvature, similarly to curvature flows. In addition, we automatically determine a set of keyframes that best represent this interpolated transformation curve, and can be used subsequently, to manually refine the color grade. We show how our method can successfully transfer color palettes between videos for a range of visual styles and a number of input video clips.
A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation

This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide 'best' car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.
Global H∞ Consensus of Multi-Agent Systems with Lipschitz Nonlinear Dynamics

This paper addresses the global consensus problems of a class of nonlinear multi-agent systems with Lipschitz nonlinearity and directed communication graphs, by using a distributed consensus protocol based on the relative states of neighboring agents. A two-step algorithm is presented to construct a protocol, under which a Lipschitz multi-agent system without disturbances can reach global consensus for a strongly connected directed communication graph. Another algorithm is then given to design a protocol which can achieve global consensus with a guaranteed H∞ performance for a Lipschitz multiagent system subject to external disturbances. The case with a leader-follower communication graph is also discussed. Finally, the effectiveness of the theoretical results is demonstrated through a network of single-link manipulators.
Lightweight Prevention of Architectural Erosion

Avoiding architectural erosion helps extend the lifetime of an evolving software system. Erosion can be reduced by ensuring that (i) developers share a good understanding of a system’s architecture; (ii) alignment is preserved between the architectural description and its implementation at all stages of system construction and maintenance; and (iii) architectural changes are treated with the same care and attention as the production of the initial design. Through the metaphor of ‘agile development’ this paper presents a lightweight approach to the control of architectural erosion. In particular, it covers the representation of an architectural description and the management of alignment between description and implementation during system evolution. A prototype support tool, ArchAngel, is introduced. This maintains an architectural design description, identifies when changes occur with respect to that description, and reports these changes for evaluation.
Compendium of physical activities: an update of activity codes and MET intensities.

We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.
Incognito: Efficient Full-Domain K-Anonymity

A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes that clearly identify individuals, such as Name and Social Security Number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zipcode, Sex, and Birthdate to re-identify individuals who were supposed to remain anonymous. "Joining" attacks are made easier by the availability of other, complementary, databases over the Internet.K-anonymization is a technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In this paper, we provide a practical framework for implementing one model of k-anonymization, called full-domain generalization. We introduce a set of algorithms for producing minimal full-domain generalizations, and show that these algorithms perform up to an order of magnitude faster than previous algorithms on two real-life databases.Besides full-domain generalization, numerous other models have also been proposed for k-anonymization. The second contribution in this paper is a single taxonomy that categorizes previous models and introduces some promising new alternatives.
Exploring Stereotypes and Biased Data with the Crowd

In 2016, Baidu and Google spent somewhere between twenty and thirty billion dollars developing and acquiring artificial intelligence and machine learning technologies (Bughin et al. 2017). A range of other sectors, including health care, education, and manufacturing, are also predicted to adopt these technologies at increasing rates. Machine learning and AI are proven to have the capacity to greatly improve lives and spur innovation. However, as society becomes increasingly dependent on these technologies, it is crucial that we acknowledge some of the dangers, including the capacity for these algorithms to absorb and amplify harmful cultural biases. Algorithms are often praised for their objectivity, but machine learning algorithms have increasingly made news for a number of problematic outcomes, ranging from Google Photos incorrectly classifying African Americans as gorillas to the judicial system using algorithms that are biased against African Americans (Dougherty 2015; Angwin et al. 2016). These harmful outcomes can be traced back to the data that was used to train the models. Machine learning applications put a heavy premium on data quantity. Research communities generally believe that the more training data there is, the better the learning outcome of the models will be (Halevy, Norvig, and Pereira 2009). This has led to large scale data collection. However, unless extra care is taken by the researchers, these large data sets will often contain bias that can profoundly change the learning outcome. Even minimal bias within a data set can end up being amplified by machine learning models, leading to skewed results. Researchers have found that widely used image data sets imSitu and MSCOCO, along with textual data sets mined from Google News, contain significant gender bias (Zhao et al. 2017; Bolukbasi et al. 2016). This research also found that training models with this data amplified the bias in the final outcomes. Once these algorithms have been improperly trained they can then be implemented into feedback loops where systems “define their own reality and use it to justify their results” as
An efficient and flexible host-FPGA PCIe communication library

A high-performance interconnection between a host processor and FPGA accelerators is in much demand. Among various interconnection methods, a PCIe bus is an attractive choice for loosely coupled accelerators. Because there is no standard host-FPGA communication library, FPGA developers have to write significant amounts of PCIe related code at both the FPGA side and the host processor side. A high-performance host-FPGA PCIe communication library holds the key to broadening the use of FPGA accelerators. In this paper we target efficiency and flexibility as two important features in such a library. We discuss the challenges in providing these features, and present our solution to these challenges. We propose EPEE, an efficient and flexible host-FPGA PCIe communication library and describe its design. We implemented EPEE in various generations of Xilinx FPGAs with up to 26.24 Gbps half-duplex and 43.02 Gbps full-duplex aggregate throughput in the PCIe Gen2 X8 mode; these are at the best utilization levels that a host-FPGA PCIe library can achieve. The EPEE library has been integrated into four different FPGA applications with different data usage patterns in various institutes.
Diet and risk of colorectal cancer in a cohort of Finnish men

Based on previous epidemiological studies, high fat and meat consumption may increase and fiber, calcium, and vegetable consumption may decrease the risk of colorectal cancer. We sought to address these hypotheses in a male Finnish cohort.  We analyzed data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC Study) where 27, 111 male smokers completed a validated dietary questionnaire at baseline. After an average of 8 years of follow-up, we documented 185 cases of colorectal cancer. The analyses were carried out using the Cox proportional hazards model.  The relative risk (RR) for men in the highest quartile of calcium intake compared with men in the lowest quartile was 0.6 (95% CI 0.4–0.9, p for trend 0.04). Likewise, the intake of milk protein and the consumption of milk products was inversely associated with risk of colorectal cancer. However, intake of dietary fiber was not associated with risk, nor was fat intake. Consumption of meat or different types of meat, and fried meat, fruits or vegetables were not associated with risk.  In this cohort of men consuming a diet high in fat, meat, and fiber and low in vegetables, high calcium intake was associated with lowered risk of colorectal cancer.
Kintinuous: Spatially Extended KinectFusion

In this paper we present an extension to the KinectFusion algorithm that permits dense mesh-based mapping of extended scale environments in real-time. This is achieved through (i) altering the original algorithm such that the region of space being mapped by the KinectFusion algorithm can vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusion volume due to this variation, and, (iii) incrementally adding the resulting points to a triangular mesh representation of the environment. The system is implemented as a set of hierarchical multi-threaded components which are capable of operating in real-time. The architecture facilitates the creation and integration of new modules with minimal impact on the performance on the dense volume tracking and surface reconstruction modules. We provide experimental results demonstrating the system’s ability to map areas considerably beyond the scale of the original KinectFusion algorithm including a two story apartment and an extended sequence taken from a car at night. In order to overcome failure of the iterative closest point (ICP) based odometry in areas of low geometric features we have evaluated the Fast Odometry from Vision (FOVIS) system as an alternative. We provide a comparison between the two approaches where we show a trade off between the reduced drift of the visual odometry approach and the higher local mesh quality of the ICP-based approach. Finally we present ongoing work on incorporating full simultaneous localisation and mapping (SLAM) pose-graph optimisation.
Communication-Efficient Learning of Deep Networks from Decentralized Data

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10–100× as compared to synchronized stochastic gradient descent.
: Evidence from the Field ∗

The research in Psychology and Economics (a.k.a. Behavioral Economics) suggests that individuals deviate from the standard model in three respects: (i) non-standard preferences; (ii) non-standard beliefs; and (iii) non-standard decision-making. In this paper, I survey the empirical evidence from the field on these three classes of deviations. The evidence covers a number of applications, from consumption to finance, from crime to voting, from charitable giving to labor supply. In the class of non-standard preferences, I discuss time preferences (self-control problems), risk preferences (reference dependence), and social preferences. On non-standard beliefs, I present evidence on overconfidence, on the law of small numbers, and on projection bias. Regarding non-standard decision-making, I cover framing, limited attention, menu effects, persuasion and social pressure, and emotions. I also present evidence on how rational actors– firms, employers, CEOs, investors, and politicians–respond to the non-standard behavior described in the survey. I then summarize five common empirical methodologies used in Psychology and Economics. Finally, I briefly discuss under what conditions experience and market interactions limit the impact of the non-standard features. ∗I would like to thank Roger Gordon (the editor), three exceptionally careful referees, Dan Acland, Malcolm Baker, Brad Barber, Nicholas Barberis, Dan Benjamin, Saurabh Bhargava, Colin Camerer, David Card, Raj Chetty, James Choi, Sanjit Dhami, Constanca Esteves, Ernst Fehr, Shane Frederick, Drew Fudenberg, David Hirshleifer, Eric Johnson, Lawrence F. Katz, Georg Kirchsteiger, Jeffrey Kling, Howard Kunreuther, David Laibson, George Loewenstein, Erzo F.P. Luttmer, Rosario Macera, Ulrike Malmendier, MichelAndre Marechal, John Morgan, Ted O’Donoghue, Ignacio Palacios-Huerta, Joshua Palmer, Vikram Pathania, Matthew Rabin, Ricardo Reis, Uri Simonsohn, Rani Spiegler, Bjarne Steffen, Justin Sydnor, Richard Thaler, Jeremy Tobacman, Michael Urbancic, Ebonya Washington, Kathryn Zeiler, and Jonathan Zinman for useful comments and suggestions. Thomas Barrios, Charles Lin, and Anitha Sivasankaran provided excellent research assistance. I also want to thank the students of my Psychology and Economics graduate class who over the years helped shape the ideas in this paper. Finally, I would like to express all my gratitude to David Laibson and Matthew Rabin for their exceptional generosity in sharing their insights with the next generations of behavioral economists. This paper would not have been possible without them.
A Novel millimeter-wave Substrate Integrated Waveguide (SIW) filter buried in LTCC

In this work, we propose a novel millimeter-wave SIW bandpass filter (BPF) that combines the technology of E-plane iris filter, SIW and LTCC. This filter with equivalent E-plane iris mounted in SIW is buried in LTCC substrates, and has a pass bandwidth from 34.1GHz to 35.9GHz. The insertion loss is less than 1.8dB and the return loss is better than 21dB in the entire pass band.
Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach

Understanding traffic scene images taken from vehicle mounted cameras is important for high-level tasks, such as advanced driver assistance systems and autonomous driving. It is a challenging problem due to large variations under different weather or illumination conditions. In this paper, we tackle the problem of traffic scene understanding from a cross-domain perspective. We attempt to understand the traffic scene from images taken from the same location but under different weather or illumination conditions (e.g., understanding the same traffic scene from images on a rainy night with the help of images taken on a sunny day). To this end, we propose a dense correspondence-based transfer learning (DCTL) approach, which consists of three main steps: 1) extracting deep representations of traffic scene images via a fine-tuned convolutional neural network; 2) constructing compact and effective representations via cross-domain metric learning and subspace alignment for cross-domain retrieval; and 3) transferring the annotations from the retrieved best matching image to the test image based on cross-domain dense correspondences and a probabilistic Markov random field. To verify the effectiveness of our DCTL approach, we conduct extensive experiments on a challenging data set, which contains 1828 images from six weather or illumination conditions.