nils-herrmann/clean_scirepeval huggingface dataset:
A 92%-efficiency wide-input-voltage-range switched-capacitor DC-DC converter

The traditional inductor-based buck converter has been the dominant design for step-down switched-mode voltage regulators for decades. Switched-capacitor (SC) DC-DC converters, on the other hand, have traditionally been used in low- power (<;10mW) and low-conversion-ratio (<;4:1) applications where neither regulation nor efficiency is critical. However, a number of SC converter topologies are very effective in their utilization of switches and passive elements, especially in relation to the ever-popular buck converters [1,2,5]. This work encompasses the complete design, fabrication, and test of a CMOS-based switched-capacitor DC-DC converter, addressing the ubiquitous 12 to 1.5V board-mounted point-of-load application. In particular, the circuit developed in this work attains higher efficiency (92% peak, and >;80% over a load range of 5mA to 1A) than surveyed competitive buck converters, while requiring less board area and less costly passive components. The topology and controller enable a wide input voltage (V!N) range of 7.5 to 13.5V with an output voltage (Vοuτ) of 1.5V Control techniques based on feedback and feedforward provide tight regulation (30mVpp) under worst-case load-step (1A) conditions. This work shows that SC converters can outperform buck converters, and thus the scope of SC converter applications can and should be expanded.
EL Cine del Futuro: The Cinema of the Future

Presence Volume /, Number 3. Summer / 992 Reprinted from Espacios 23-24, 1955. ' studio is hastily converting to its own "revolutionär)'" system—Cinerama, Colorama, Panoramic Screen, Cinemascope, Three-D, and Stereophonic Sound. A dozen marquees in Time Square are luring customers into the realm of a "sensational new experience." Everywhere we see the "initiated" holding pencils before the winked eyes of the "uninitiated" explaining the mysteries of 3-D. The critics are lining up pro and con concluding their articles profoundly with "after all, it's the story that counts." Along with other filmgoers desiring orientation, I have been reading these articles and have sadly discovered that they reflect this confusion rather than illuminate it. It is apparent that the inability to cope with the problem stems from a refusal to adopt a wider frame of reference, and from a meager understanding of the place art has in life generally. All living things engage, on a higher or lower level, in a continuous cycle of orientation and action. For example, an animal on a mountain ledge hears a rumbling sound and sees an avalanche of rocks descending on it. It cries with
An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning

The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.
DRAMsim: a memory system simulator

As memory accesses become slower with respect to the processor and consume more power with increasing memory size, the focus of memory performance and power consumption has become increasingly important. With the trend to develop multi-threaded, multi-core processors, the demands on the memory system will continue to scale. However, determining the optimal memory system configuration is non-trivial. The memory system performance is sensitive to a large number of parameters. Each of these parameters take on a number of values and interact in fashions that make overall trends difficult to discern. A comparison of the memory system architectures becomes even harder when we add the dimensions of power consumption and manufacturing cost. Unfortunately, there is a lack of tools in the public-domain that support such studies. Therefore, we introduce DRAMsim, a detailed and highly-configurable C-based memory system simulator to fill this gap. DRAMsim implements detailed timing models for a variety of existing memories, including SDRAM, DDR, DDR2, DRDRAM and FB-DIMM, with the capability to easily vary their parameters. It also models the power consumption of SDRAM and its derivatives. It can be used as a standalone simulator or as part of a more comprehensive system-level model. We have successfully integrated DRAMsim into a variety of simulators including MASE , Sim-alpha , BOCHS[2] and GEMS[13]. The simulator can be downloaded from www.ece.umd.edu/dramsim.
A new interpretation of nonlinear energy operator and its efficacy in spike detection

A nonlinear energy operator (NEO) gives an estimate of the energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. Here, the authors give a new interpretation of NEO and extend its use in stochastic signals. They show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEGs).
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming

Recent approaches to causal discovery based on Boolean satisfiability solvers have opened new opportunities to consider search spaces for causal models with both feedback cycles and unmeasured confounders. However, the available methods have so far not been able to provide a principled account of how to handle conflicting constraints that arise from statistical variability. Here we present a new approach that preserves the versatility of Boolean constraint solving and attains a high accuracy despite the presence of statistical errors. We develop a new logical encoding of (in)dependence constraints that is both well suited for the domain and allows for faster solving. We represent this encoding in Answer Set Programming (ASP), and apply a state-of-theart ASP solver for the optimization task. Based on different theoretical motivations, we explore a variety of methods to handle statistical errors. Our approach currently scales to cyclic latent variable models with up to seven observed variables and outperforms the available constraintbased methods in accuracy.
Model predictive parking control for nonholonomic vehicles using time-state control form

In this study, a model predictive control (MPC) for nonholonomic vehicles with steering and travel range constraints is proposed and applied to the parking problem. We transformed the nonlinear dynamics into two linear subsystems using time-state control form (TSCF) where we suppress the computational effort of MPC with reduced order dynamics. Both steering and travel range constraints can be considered using 1st order approximation with enough accuracy. The parking problem for nonholonomic vehicles needs a switching movement where the switching point is automatically optimized. The performance of the proposed method is verified through actual vehicle experiments on the parking problem in narrow garage.
SSH: Single Stage Headless Face Detector

We introduce the Single Stage Headless (SSH) face detector. Unlike two stage proposal-classification detectors, SSH detects faces in a single stage directly from the early convolutional layers in a classification network. SSH is headless. That is, it is able to achieve state-of-the-art results while removing the “head” of its underlying classification network – i.e. all fully connected layers in the VGG-16 which contains a large number of parameters. Additionally, instead of relying on an image pyramid to detect faces with various scales, SSH is scale-invariant by design. We simultaneously detect faces with different scales in a single forward pass of the network, but from different layers. These properties make SSH fast and light-weight. Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset. Even though, unlike the current state-of-the-art, SSH does not use an image pyramid and is 5X faster. Moreover, if an image pyramid is deployed, our light-weight network achieves state-of-the-art on all subsets of the WIDER dataset, improving the AP by 2.5%. SSH also reaches state-of-the-art results on the FDDB and Pascal-Faces datasets while using a small input size, leading to a speed of 50 frames/second on a GPU.
Analytical model for LLC resonant converter with variable duty-cycle control

In LLC resonant converters, the variable duty-cycle control is usually combined with a variable frequency control to widen the gain range, improve the light-load efficiency, or suppress the inrush current during start-up. However, a proper analytical model for the variable duty-cycle controlled LLC converter is still not available due to the complexity of operation modes and the nonlinearity of steady-state equations. This paper makes the efforts to develop an analytical model for the LLC converter with variable duty-cycle control. All possible operation models and critical operation characteristics are identified and discussed. The proposed model enables a better understanding of the operation characteristics and fast parameter design of the LLC converter, which otherwise cannot be achieved by the existing simulation based methods and numerical models. The results obtained from the proposed model are in well agreement with the simulations and the experimental verifications from a 500-W prototype.
Shape Reconstruction in Projective Grid Space from Large Number of Images

This paper proposes a new scheme for multi-image projective reconstruction based on a projective grid space. The projective grid space is defined by two basis views and the fundamental matrix relating these views. Given fundamental matrices relating other views to each of the two basis views, this projective grid space can be related to any view. In the projective grid space as a general space that is related to all images, a projective shape can be reconstructed from all the images of weakly calibrated cameras. The projective reconstruction is one way to reduce the effort of the calibration because it does not need Euclid metric information, but rather only correspondences of several points between the images. For demonstrating the effectiveness of the proposed projective grid definition, we modify the voxel coloring algorithm for the projective voxel scheme. The quality of the virtual view images re-synthesized from the projective shape demonstrates the effectiveness of our proposed scheme for projective reconstruction from a large number of images.
Methodologies, tools and languages for building ontologies: Where is their meeting point?

In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available.
Predicting the popularity of micro-reviews: A Foursquare case study

We tackle the problem of predicting the future popularity level of micro-reviews, focusing on Foursquare tips, whose high degree of informality and briefness offer extra difficulties to the design of effective popularity prediction methods. Such predictions can greatly benefit the future design of content filtering and recommendation methods. Towards our goal, we first propose a rich set of features related to the user who posted the tip, the venue where it was posted, and the tip’s content to capture factors that may impact popularity of a tip. We evaluate different regression and classification based models using this rich set of proposed features as predictors in various scenarios. As fas as we know, this is the first work to investigate the predictability of micro-review popularity (or helpfulness) exploiting spatial, temporal, topical and, social aspects that are rarely exploited conjointly in this domain.
Computational Intelligence Based QoS-Aware Web Service Composition: A Systematic Literature Review

Web service composition concerns the building of new value added services by integrating the sets of existing web services. Due to the seamless proliferation of web services, it becomes difficult to find a suitable web service that satisfies the requirements of users during web service composition. This paper systematically reviews existing research on QoS-aware web service composition using computational intelligence techniques (published between 2005 and 2015). This paper develops a classification of research approaches on computational intelligence based QoS-aware web service composition and describes future research directions in this area. In particular, the results of this study confirms that new meta-heuristic algorithms have not yet been applied for solving QoS-aware web services composition.
End-to-end protocols for Cognitive Radio Ad Hoc Networks: An evaluation study

Cognitive radio ad hoc networks (CRAHNs) constitute a viable solution to solve the current problems of inefficiency in the spectrum allocation, and to deploy highly reconfigurable and self-organizingwireless networks. Cognitive radio (CR) devices are envisaged to utilize the spectrum in an opportunistic way by dynamically accessing different licensed portions of the spectrum. To this aim, most of the recent research has mainly focused on devising spectrum sensing and sharing algorithms at the link layer, so that CR devices can operate without interfering with the transmissions of other licensed users, also called primary users (PUs). However, it is also important to consider the impact of such schemes on the higher layers of the protocol stack, in order to provide efficient end-to-end data delivery. At present, routing and transport layer protocols constitute an important yet not deeply investigated area of research over CRAHNs. This paper provides three main contributions on the modeling and performance evaluation of end-to-end protocols (e.g. routing and transport layer protocols) for CRAHNs. First, we describe NS2-CRAHN, an extension of the NS-2 simulator, which is designed to support realistic simulation of CRAHNs. NS2-CRAHN contains an accurate yet flexible modeling of the activities of PUs and of the cognitive cycle implemented by each CR user. Second, we analyze the impact of CRAHNs characteristics over the route formation process, by considering different routing metrics and route discovery algorithms. Finally, we study TCP performance over CRAHNs, by considering the impact of three factors ondifferent TCP variants: (i) spectrumsensing cycle, (ii) interference from PUs and (iii) channel heterogeneity. Simulation results highlight the differences of CRAHNs with traditional ad hoc networks and provide useful directions for the design of novel end-to-end protocols for CRAHNs.
Static and Dynamic 4-Way Handshake Solutions to Avoid Denial of Service Attack in Wi-Fi Protected Access and IEEE 802.11i

This paper focuses on WPA and IEEE 802.11i protocols that represent two important solutions in the wireless environment. Scenarios where it is possible to produce a DoS attack and DoS flooding attacks are outlined. The last phase of the authentication process, represented by the 4-way handshake procedure, is shown to be unsafe from DoS attack. This can produce the undesired effect of memory exhaustion if a flooding DoS attack is conducted. In order to avoid DoS attack without increasing the complexity of wireless mobile devices too much and without changing through some further control fields of the frame structure of wireless security protocols, a solution is found and an extension of WPA and IEEE 802.11 is proposed. A protocol extension with three “static” variants and with a resource-aware dynamic approach is considered. The three enhancements to the standard protocols are achieved through some simple changes on the client side and they are robust against DoS and DoS flooding attack. Advantages introduced by the proposal are validated by simulation campaigns and simulation parameters such as attempted attacks, successful attacks, and CPU load, while the algorithm execution time is evaluated. Simulation results show how the three static solutions avoid memory exhaustion and present a good performance in terms of CPU load and execution time in comparison with the standard WPA and IEEE 802.11i protocols. However, if the mobile device presents different resource availability in terms of CPU and memory or if resource availability significantly changes in time, a dynamic approach that is able to switch among three different modalities could be more suitable.
Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy.
Cryptocurrency price drivers: Wavelet coherence analysis revisited

Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.
Survey of Security Advances in Smart Grid: A Data Driven Approach

With the integration of advanced computing and communication technologies, smart grid is considered as the next-generation power system, which promises self healing, resilience, sustainability, and efficiency to the energy critical infrastructure. The smart grid innovation brings enormous challenges and initiatives across both industry and academia, in which the security issue emerges to be a critical concern. In this paper, we present a survey of recent security advances in smart grid, by a data driven approach. Compared with existing related works, our survey is centered around the security vulnerabilities and solutions within the entire lifecycle of smart grid data, which are systematically decomposed into four sequential stages: 1) data generation; 2) data acquisition; 3) data storage; and 4) data processing. Moreover, we further review the security analytics in smart grid, which employs data analytics to ensure smart grid security. Finally, an effort to shed light on potential future research concludes this paper.
Efficient Multi-label Classification with Many Labels

In multi-label classification, each sample can be associated with a set of class labels. When the number of labels grows to the hundreds or even thousands, existing multi-label classification methods often become computationally inefficient. In recent years, a number of remedies have been proposed. However, they are based either on simple dimension reduction techniques or involve expensive optimization problems. In this paper, we address this problem by selecting a small subset of class labels that can approximately span the original label space. This is performed by an efficient randomized sampling procedure where the sampling probability of each class label reflects its importance among all the labels. Experiments on a number of realworld multi-label data sets with many labels demonstrate the appealing performance and efficiency of the proposed algorithm.
Wideband diplexed feed chains for FSS + BSS applications

Three different RF chain architectures operating in the FSS (Fixed Satellite Services) + BSS (Broadcast Satellite) spectrum are presented and discussed. The RF chains are based on a common wideband corrugated feed horn, but differ on the approach used for bands and polarizations separation. A breadboard of a novel self-diplexed configuration has been designed, manufactured and tested. It proves to be the preferred candidate for bandwidth, losses and power handling. Very good correlation of the RF performance to the theoretical design is found.
Bayesian Factorization Machines

This work presents simple and fast structured Bayesian learning for matrix and tensor factorization models. An unblocked Gibbs sampler is proposed for factorization machines (FM) which are a general class of latent variable models subsuming matrix, tensor and many other factorization models. We empirically show on the large Netflix challenge dataset that Bayesian FM are fast, scalable and more accurate than state-of-the-art factorization models.