nils-herrmann/clean_scirepeval huggingface dataset:
A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition

The majority of the research related to visual recognition has so far focused on bottom-up analysis, where the input is processed in a cascade of cortical regions that analyze increasingly complex information. Gradually more studies emphasize the role of top-down facilitation in cortical analysis, but it remains something of a mystery how such processing would be initiated. After all, top-down facilitation implies that high-level information is activated earlier than some relevant lower-level information. Building on previous studies, I propose a specific mechanism for the activation of top-down facilitation during visual object recognition. The gist of this hypothesis is that a partially analyzed version of the input image (i.e., a blurred image) is projected rapidly from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC expectations about the most likely interpretations of the input image, which are then back-projected as an initial guess to the temporal cortex to be integrated with the bottom-up analysis. The top-down process facilitates recognition by substantially limiting the number of object representations that need to be considered. Furthermore, such a rapid mechanism may provide critical information when a quick response is necessary.
A Survey of Botnet Technology and Defenses

Global Internet threats have undergone a profound transformation from attacks designed solely to disable infrastructure to those that also target people and organizations. At the center of many of these attacks are collections of compromised computers, or Botnets, remotely controlled by the attackers, and whose members are located in homes, schools, businesses, and governments around the world [6]. In this survey paper we provide a brief look at how existing botnet research, the evolution and future of botnets, as well as the goals and visibility of today’s networks intersect to inform the field of botnet technology and defense.
Design and Analysis of a Robust Broadcast Scheme for VANET Safety-Related Services

IEEE- and ASTM-adopted dedicated short-range communications (DSRC) standards are key enabling technologies for the next generation of vehicular safety communications. Vehicle-safety-related communication services, which require reliable and fast message delivery, usually demand broadcast communications in vehicular ad hoc networks (VANETs). In this paper, we propose and justify a distributive cross-layer scheme for the design of the control channel in DSRC with three levels of broadcast services that are critical to most potential vehicle-safety-related applications. The new scheme for enhancing broadcast reliability includes preemptive priority in safety services, dynamic receiver-oriented packet repetitions for one-hop emergency warning message dissemination, a multifrequency busy tone and minislot within the distributed interframe space (DIFS) in IEEE 802.11, and robust distance-based relay selection for multihop broadcast of emergency notification messages. Compared with a current draft of IEEE 802.11p and other schemes for DSRC safety-related services, the scheme proposed in this paper is more robust and scalable and easy to implement. Additionally, we investigate the reliability and performance of the proposed broadcast scheme for DSRC VANET safety-related services on the highway analytically and by simulations. The analytic model accounts for the impact of the hidden terminal problem, the fading channel conditions, varied message arrival intervals, and the backoff counter process on reliability and performance.
Spacetime faces: high resolution capture for modeling and animation

We present an end-to-end system that goes from video sequences to high resolution, editable, dynamically controllable face models. The capture system employs synchronized video cameras and structured light projectors to record videos of a moving face from multiple viewpoints. A novel spacetime stereo algorithm is introduced to compute depth maps accurately and overcome over-fitting deficiencies in prior work. A new template fitting and tracking procedure fills in missing data and yields point correspondence across the entire sequence without using markers. We demonstrate a data-driven, interactive method for inverse kinematics that draws on the large set of fitted templates and allows for posing new expressions by dragging surface points directly. Finally, we describe new tools that model the dynamics in the input sequence to enable new animations, created via key-framing or texture-synthesis techniques.
Live Video Analytics at Scale with Approximation and Delay-Tolerance

Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live videos in realtime. We describe VideoStorm, a video analytics system that processes thousands of video analytics queries on live video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of video analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm’s offline profiler generates query resourcequality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7× better lag, processing video from operational traffic cameras.
Examining Two Expectation Disconfirmation Theory Models: Assimilation and Asymmetry Effects

Research Article Nancy K. Lankton Marshall University lankton@marshall.edu Harrison D. McKnight Michigan State University mcknight@bus.msu.edu Expectation disconfirmation theory (EDT) posits that expectations, disconfirmation, and performance influence customer satisfaction. While information systems researchers have adopted EDT to explain user information technology (IT) satisfaction, they often use various EDT model subsets. Leaving out one or more key variables, or key relationships among the variables, can reduce EDT’s explanatory potential. It can also suggest an intervention for practice that is very different from (and inferior to) the intervention suggested by a more complete model. Performance is an especially beneficial but largely neglected EDT construct in IT research. Using EDT theory from the marketing literature, this paper explains and demonstrates the incremental value of using the complete IT EDT model with performance versus the simplified model without it. Studying software users, we find that the complete model with performance both reveals assimilation effects for less experienced users and uncovers asymmetric effects not found in the simplified model. We also find that usefulness performance more strongly influences usage continuance intention than does any other EDT variable. We explain how researchers and practitioners can take full advantage of the predictive and explanatory power of the complete IT EDT model.
A 24-GHz CMOS UWB Radar Transmitter With Compressed Pulses

A fully integrated 24-GHz CMOS ultra-wideband (UWB) radar transmitter for short-range automotive application is presented. For high-range resolution and improved signal-to-noise ratio, a pulse compression technique using binary phase code is adopted. Design issues of UWB radar transmitter are investigated based on fundamental pulse theory. A pulse former, which operates as a switch to generate a pulse modulated carrier signal and a bi-phase modulator for pulse compression, is proposed. The proposed transmitter achieves 4-GHz output signal bandwidth, which means a minimum range resolution of 7.5 cm, and the total dc power dissipation is 63 mW.
Effect of the damping function in dispersion corrected density functional theory

It is shown by an extensive benchmark on molecular energy data that the mathematical form of the damping function in DFT-D methods has only a minor impact on the quality of the results. For 12 different functionals, a standard "zero-damping" formula and rational damping to finite values for small interatomic distances according to Becke and Johnson (BJ-damping) has been tested. The same (DFT-D3) scheme for the computation of the dispersion coefficients is used. The BJ-damping requires one fit parameter more for each functional (three instead of two) but has the advantage of avoiding repulsive interatomic forces at shorter distances. With BJ-damping better results for nonbonded distances and more clear effects of intramolecular dispersion in four representative molecular structures are found. For the noncovalently-bonded structures in the S22 set, both schemes lead to very similar intermolecular distances. For noncovalent interaction energies BJ-damping performs slightly better but both variants can be recommended in general. The exception to this is Hartree-Fock that can be recommended only in the BJ-variant and which is then close to the accuracy of corrected GGAs for non-covalent interactions. According to the thermodynamic benchmarks BJ-damping is more accurate especially for medium-range electron correlation problems and only small and practically insignificant double-counting effects are observed. It seems to provide a physically correct short-range behavior of correlation/dispersion even with unmodified standard functionals. In any case, the differences between the two methods are much smaller than the overall dispersion effect and often also smaller than the influence of the underlying density functional.
EXPLORING INTUITION AND ITS ROLE IN MANAGERIAL DECISION MAKING

ion in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11: 45–58. Chen, S., & Chaiken, S. 1999. The heuristic-systematic model in its broader context. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology: 73–96.  Chi, M. T. H., Glaser, R., & Farr, M. J. 1998. The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates. Claxton, G. 1998. Knowing without knowing why. Psychologist, 11(5): 217–220. Collins, H. M. 1982. The replication of experiments in physics. In B. Barnes & D. Edge (Eds.), Science in context: 94–116. Cambridge, MA: MIT Press. Cyert, R. M., & March,  A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall. Dawes, R. M., Faust, D., & Meehl, P. E. 1989. Clinical versus actuarial judgment.  1668–1674. De Dreu, C. K. W. 2003. Time pressure and closing of the mind in negotiation. Organizational Behavior and Human Decision Processes, 91: 280–295. Denes-Raj, V., & Epstein, S. 1994. Conflict between intuitive and rational processing: When people behave against their better judgment. Journal of Personality and Social Psychology, 66: 819–829. Donaldson, T. 2003.  Taking ethics seriously—A mission now more possible. Academy of Management Review, 28: 363–366. Dreyfus, H. L., & Dreyfus, S. E. 1986.  The power of human intuition and expertise in the era of the computer.  Judgment and decision making under time pressure. In O. Svenson & A. J. Maule (Eds.), Time pressure and stress in human judgment and decision making: 27–40.  Eisenhardt, K. 1989. Making fast strategic decisions in highvelocity environments. Academy of Management Jour-
Automated classification of Pap smear images to detect cervical dysplasia

The present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades.



The classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix. The system is evaluated on two generated databases obtained from two diagnostic centers, one containing 1610 single cervical cells and the other 1320 complete smear level images. The main objective of this database generation is to categorize the images according to the Bethesda system of classification both of which require lots of training and expertise. The system is also trained and tested on the benchmark Herlev University database which is publicly available. In this contribution a new segmentation technique has also been proposed for extracting shape features. Ripplet Type I transform, Histogram first order statistics and Gray Level Co-occurrence Matrix have been used for color and texture features respectively. To improve classification results, ensemble method is used, which integrates the decision of three classifiers. Assessments are performed using 5 fold cross validation.



Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods.



This type of automated cancer classifier will be of particular help in early detection of cancer.
Wideband Planar Folded Dipole Antenna With Self-balanced Impedance Property

A planar folded dipole antenna that exhibits wideband characteristics is proposed. The antenna has simple planar construction without a ground plane and is easy to be assembled. Parameter values are adjusted in order to obtain wideband properties and compactness by using an electromagnetic simulator based on the method of moments. An experimental result centered at 1.7 GHz for 50 impedance matching shows that the antenna has bandwidth over 55% . The gains of the antenna are almost constant (2 dBi) in this frequency band and the radiation patterns are very similar to those of a normal dipole antenna. It is also shown that the antenna has a self-balanced impedance property in this frequency band.
A dual-band unidirectional coplanar antenna for 2.4–5-GHz wireless applications

A dual-band unidirectional antenna has been developed. The dual-band antenna consists of a long dipole for the lower frequency band and two short dipoles for the higher frequency band. All dipoles are printed coplanar on a thin substrate. The printed dipole antenna is excited by a microstrip line. The higher-order mode in the higher frequency band has been suppressed, leading to a good unidirectional pattern in the both frequency bands. This dual-band unidirectional antenna may find application in base stations and/or access points for 2.4/5- GHz wireless communications.
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.
A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.
The Design of SREE - A Prototype Potential Ambiguity Finder for Requirements Specifications and Lessons Learned

Many a tool for finding ambiguities in natural language (NL) requirements specifications (RSs) is based on a parser and a parts-of-speech identifier, which are inherently imperfect on real NL text. Therefore, any such tool inherently has less than 100% recall. Consequently, running such a tool on a NL RS for a highly critical system does not eliminate the need for a complete manual search for ambiguity in the RS.  Can an ambiguity-finding tool (AFT) be built that has 100% recall on the types of ambiguities that are in the AFT’s scope such that a manual search in an RS for ambiguities outside the AFT’s scope is significantly easier than a manual search of the RS for all ambiguities?  This paper presents the design of a prototype AFT, SREE (Systemized Requirements Engineering Environment), whose goal is achieving a 100% recall rate for the ambiguities in its scope, even at the cost of a precision rate of less than 100%. The ambiguities that SREE searches for by lexical analysis are the ones whose keyword indicators are found in SREE’s ambiguity-indicator corpus that was constructed based on studies of several industrial strength RSs. SREE was run on two of these industrial strength RSs, and the time to do a completely manual search of these RSs is compared to the time to reject the false positives in SREE’s output plus the time to do a manual search of these RSs for only ambiguities not in SREE’s scope.  SREE does not achieve its goals. However, the time comparison shows that the approach to divide ambiguity finding between an AFT with 100% recall for some types of ambiguity and a manual search for only the other types of ambiguity is promising enough to justify more work to improve the implementation of the approach. Some specific improvement suggestions are offered.
Beyond spatial pyramids: Receptive field learning for pooled image features

In this paper we examine the effect of receptive field designs on classification accuracy in the commonly adopted pipeline of image classification. While existing algorithms usually use manually defined spatial regions for pooling, we show that learning more adaptive receptive fields increases performance even with a significantly smaller codebook size at the coding layer. To learn the optimal pooling parameters, we adopt the idea of over-completeness by starting with a large number of receptive field candidates, and train a classifier with structured sparsity to only use a sparse subset of all the features. An efficient algorithm based on incremental feature selection and retraining is proposed for fast learning. With this method, we achieve the best published performance on the CIFAR-10 dataset, using a much lower dimensional feature space than previous methods.
Advanced Induction Motor Endring Design Features for High Speed Applications

This paper presents advancements in induction motor endring design to overcome mechanical limitations and extend the operating speed range and joint reliability of induction machines. A novel endring design met the challenging mechanical requirements of this high speed, high temperature, power dense application, without compromising electrical performance. Analysis is presented of the advanced endring design features including a non uniform cross section, hoop stress relief cuts, and an integrated joint boss, which reduced critical stress concentrations, allowing operation under a broad speed and temperature design range. A generalized treatment of this design approach is presented comparing the concept results to conventional design techniques. Additionally, a low temperature joining process of the bar/end ring connection is discussed that provides the required joint strength without compromising the mechanical strength of the age hardened parent metals. A description of a prototype 2 MW, 15,000 rpm flywheel motor generator embodying this technology is presented
Survey of Machine Learning Algorithms for Disease Diagnostic

In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of research. In recent years, significant attempts are made for the enhancement of computer aided diagnosis applications because errors in medical diagnostic systems can result in seriously misleading medical treatments. Machine learning is important in Computer Aided Diagnosis. After using an easy equation, objects such as organs may not be indicated accurately. So, pattern recognition fundamentally involves learning from examples. In the field of bio-medical, pattern recognition and machine learning promise the improved accuracy of perception and diagnosis of disease. They also promote the objectivity of decision-making process. For the analysis of high-dimensional and multimodal bio-medical data, machine learning offers a worthy approach for making classy and automatic algorithms. This survey paper provides the comparative analysis of different machine learning algorithms for diagnosis of different diseases such as heart disease, diabetes disease, liver disease, dengue disease and hepatitis disease. It brings attention towards the suite of machine learning algorithms and tools that are used for the analysis of diseases and decision-making process accordingly.
The alldifferent Constraint: A Survey

The constraint of difference is known to the constraint programming community since Lauriere introduced Alice in 1978. Since then, several strategies have been designed to solve the alldifferent constraint. This paper surveys the most important developments over the years regarding the alldifferent constraint. First we summarize the underlying concepts and results from graph theory and integer programming. Then we give an overview and an abstract comparison of different solution strategies. In addition, the symmetric alldifferent constraint is treated. Finally, we show how to apply cost-based filtering to the alldifferent constraint. A preliminary version of this paper appeared as .