nils-herrmann/clean_scirepeval huggingface dataset:
28/38-GHz dual-band millimeter wave SIW array antenna with EBG structures for 5G applications

The design of linearly polarized dual-band substrate integrated waveguide (SIW) antenna/array operating at Ka-band is proposed. The single antenna element consists of a SIW cavity with two longitudinal slots engraved in one of the conducting planes. The longer and shorter slots are resonating at 28 GHz and 38 GHz, respectively. Only the simulated results are presented. All simulations have been carried out using industry-standard software, CST Microwave Studio. For single antenna element, an impedance bandwidth (S11 <; -10 dB) of 0.45 GHz (1.60 %) and 2.20 GHz (5.8 %) is achieved with the maximum gain of 5.2 dBi and 5.9 dBi at 28 GHz and 38 GHz, respectively. To achieve high gain, a horizontally polarized linear array of four elements (1 × 4) is designed. For the antenna array, a microstrip lines feed network is designed using 3-dB wilkinson power divider. At 28 GHz and 38 GHz, the impedance bandwidth is 0.32 GHz (1.14 %) and 1.9 GHz (5%) having maximum gain of 11.9 dBi and 11.2 dBi, respectively. A low loss/cost substrate, RT/Duroid 5880 is used in the proposed designs.
A Practical Analytic Model for Daylight

Sunlight and skylight are rarely rendered correctly in computer graphics. A major reason for this is high computational expense. Another is that precise atmospheric data is rarely available. We present an inexpensive analytic model that approximates full spectrum daylight for various atmospheric conditions. These conditions are parameterized using terms that users can either measure or estimate. We also present an inexpensive analytic model that approximates the effects of atmosphere (aerial perspective). These models are fielded in a number of conditions and intermediate results verified against standard literature from atmospheric science. Our goal is to achieve as much accuracy as possible without sacrificing usability.
Simple Analytical and Graphical Methods for Carrier-Based PWM-VSI Drives

This paper provides analytical and graphical methods for the study, performance evaluation, and design of the modern carrier-based pulsewidth modulators (PWM’s), which are widely employed in PWM voltage-source inverter (VSI) drives. Simple techniques for generating the modulation waves of the high-performance PWM methods are described. The two most important modulator characteristics—the current ripple and the switching losses—are analytically modeled. The graphical illustration of these often complex multivariable functions accelerate the learning process and help one understand the microscopic (per-carrier cycle) and macroscopic (per fundamental cycle) behavior of all the modern PWM methods. The analytical formulas and graphics are valuable educational tools. They also aid the design and implementation of the high-performance PWM methods.
CIEL: A Universal Execution Engine for Distributed Data-Flow Computing

This paper introduces CIEL, a universal execution engine for distributed data-flow programs. Like previous execution engines, CIEL masks the complexity of distributed programming. Unlike those systems, a CIEL job can make data-dependent control-flow decisions, which enables it to compute iterative and recursive algorithms. We have also developed Skywriting, a Turingcomplete scripting language that runs directly on CIEL. The execution engine provides transparent fault tolerance and distribution to Skywriting scripts and highperformance code written in other programming languages. We have deployed CIEL on a cloud computing platform, and demonstrate that it achieves scalable performance for both iterative and non-iterative algorithms.
Throughput comparison of random access methods for M2M service over LTE networks

It is one of the key issues in the 4-th generation (4G) cellular networks how to efficiently handle the heavy random access (RA) load caused by newly accommodating the huge population of Machine-to-Machine or Machine-Type Communication (M2M or MTC) customers/devices. We consider two major candidate methods for RA preamble allocation and management, which are under consideration for possible adoption in Long Term Evolution (LTE)-Advanced. One method, Method 1, is to completely split the set of available RA preambles into two disjoint subsets: one is for human-to-human (H2H) customers and the other for M2M customers/devices. The other method, Method 2, is also to split the set into two subsets: one is for H2H customers only whereas the other is for both H2H and M2M customers. We model and analyze the throughput performance of two methods. Our results demonstrate that there is a boundary of RA load below which Method 2 performs slightly better than Method 1 but above which Method 2 degrades throughput to a large extent. Our modeling and analysis can be utilized as a guideline to design the RA preamble resource management method.
A Secure Data Deduplication Scheme for Cloud Storage

Nowadays, more and more corporate and private users outsource their data to cloud storage providers. At the same time, recent data breach incidents make end-to-end encryption an increasingly prominent requirement. Unfortunately, semantically secure encryption schemes render various cost-effective storage optimization techniques, such as data deduplication, completely ineffective. In this paper, we present a novel encryption scheme that guarantees semantic security for unpopular data and provides weaker security and better storage and bandwidth benefits for popular data. This way, data deduplication can be effective for popular data, whilst semantically secure encryption protects unpopular content, preventing its deduplication. Transitions from one mode to the other take place seamlessly at the storage server side if and only if a file becomes popular. We show that our scheme is secure under the Symmetric External Decisional Diffie-Hellman Assumption in the random oracle model, and evaluate its performance with benchmarks and simulations.
Semi-supervised truth discovery

Accessing online information from various data sources has become a necessary part of our everyday life. Unfortunately such information is not always trustworthy, as different sources are of very different qualities and often provide inaccurate and conflicting information. Existing approaches attack this problem using unsupervised learning methods, and try to infer the confidence of the data value and trustworthiness of each source from each other by assuming values provided by more sources are more accurate. However, because false values can be widespread through copying among different sources and out-of-date data often overwhelm up-to-date data, such bootstrapping methods are often ineffective.
 In this paper we propose a semi-supervised approach that finds true values with the help of ground truth data. Such ground truth data, even in very small amount, can greatly help us identify trustworthy data sources. Unlike existing studies that only provide iterative algorithms, we derive the optimal solution to our problem and provide an iterative algorithm that converges to it. Experiments show our method achieves higher accuracy than existing approaches, and it can be applied on very huge data sets when implemented with MapReduce.
High-Confidence Off-Policy Evaluation

Many reinforcement learning algorithms use trajectories collected from the execution of one or more policies to propose a new policy. Because execution of a bad policy can be costly or dangerous, techniques for evaluating the performance of the new policy without requiring its execution have been of recent interest in industry. Such off-policy evaluation methods, which estimate the performance of a policy using trajectories collected from the execution of other policies, heretofore have not provided confidences regarding the accuracy of their estimates. In this paper we propose an off-policy method for computing a lower confidence bound on the expected return of a policy.
Multi-Antenna Downlink Channels with Limited Feedback and User Selection

We analyze the sum-rate performance of a multi- antenna downlink system carrying more users than transmit antennas, with partial channel knowledge at the transmitter due to finite rate feedback. In order to exploit multiuser diversity, we show that the transmitter must have, in addition to directional information, information regarding the quality of each channel. Such information should reflect both the channel magnitude and the quantization error. Expressions for the SINR distribution and the sum-rate are derived, and tradeoffs between the number of feedback bits, the number of users, and the SNR are observed. In particular, for a target performance, having more users reduces feedback load.
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning

We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training.
Unsupervised Learning of Probabilistic Concept Hierarchies

Fisher's Cobweb provided a well-deened framework for research on the unsupervised induction of probabilistic concept hierarchies. The system also sparked the development of many successors that extended this framework along various dimensions. In this paper, we summarize the assumptions that Cobweb embodies about the representation, organization, use, and formation of probabilistic concepts, along with experimental studies that examine its sources of power. After this, we consider three systems { Arachne, Twilix, and Oxbow { that incorporate signiicant extensions and present empirical evidence that these improve behavior. In closing, we discuss other paradigms for the unsupervised learning of probabilistic knowledge and their relation to the Cobweb framework.
Painting with Bob: assisted creativity for novices

Current digital painting tools are primarily targeted at professionals and are often overwhelmingly complex for use by novices. At the same time, simpler tools may not invoke the user creatively, or are limited to plain styles that lack visual sophistication. There are many people who are not art professionals, yet would like to partake in digital creative expression. Challenges and rewards for novices differ greatly from those for professionals. In this paper, we leverage existing works in Creativity and Creativity Support Tools (CST) to formulate design goals specifically for digital art creation tools for novices. We implemented these goals within a digital painting system, called Painting with Bob. We evaluate the efficacy of the design and our prototype with a user study, and we find that users are highly satisfied with the user experience, as well as the paintings created with our system.
Semantic Cosine Similarity

Cosine similarity is a widely implemented metric in information retrieval and related studies. This metric models a text as a vector of terms and the similarity between two texts is derived from cosine value between two texts' term vectors. Cosine similarity however still can't handle the semantic meaning of the text perfectly. This paper proposes an enhancement of cosine similarity measurement by incorporating semantic checking between dimensions of two term vectors. This strategy aims to increase the similarity value between two term vectors which contain semantic relation between their dimensions with different syntax. Experimental result shows our proposal yields a promising result.
DYNAMIC MODELING OF ONE DEGREE OF FREEDOM PNEUMATIC MUSCLE-BASED ACTUATOR FOR INDUSTRIAL APPLICATIONS

Preliminary notes Pneumatic artificial muscles belong to the group of nonconventional actuators intended for various biomedical or industrial applications. The one-DOF actuator (DOF – degree of freedom) consists of two muscles acting in opposition to each other (antagonistic connection) with simplified control scheme (stiffness control loop is excluded in order to simplify the control and to achieve as high stiffness as possible). The actuator exhibits nonlinear behaviour attributable to the compliant nature of pneumatic muscles. The muscle model is based on modified two-element muscle model consisting of a variable damper and a variable spring connected in parallel. From kinematic point of view, it is a simple mechanism with one degree of freedom (arm with added mass rotating around one revolute axis) with plane of movement being parallel to the ground. The main purpose of this model is a control design, so the stringency of accuracy criteria is lowered compared to a truth model. The model is validated using the results of dynamic experiments with real plant in laboratory conditions.
A Bayes filter based adaptive floor segmentation with homography and appearance cues

This paper proposes a robust approach for image based floor detection and segmentation from sequence of images or video. In contrast to many previous approaches, which uses a priori knowledge of the surroundings, our method uses combination of modified sparse optical flow and planar homography for ground plane detection which is then combined with graph based segmentation for extraction of floor from images. We also propose a probabilistic framework which makes our method adaptive to the changes in the surroundings. We tested our algorithm on several common indoor environment scenarios and were able to extract floor even under challenging circumstances. We obtained extremely satisfactory results in various practical scenarios such as where the floor and non floor areas are of same color, in presence of textured flooring, and where illumination changes are steep.
Dynamic modeling of a fixed-wing VTOL UAV

This paper presents a transition-flight mathematical model of a civil tilt-rotor VTOL unmanned aerial vehicle (UAV) TURAC. Forces and moments acting on the UAV body are calculated using Newton's second law. Aerodynamic effects of free airstream and propeller airstream are defined separately. CFD analyses are performed to specify aerodynamic coefficients for transitional flight regime. The trim point is mathematically defined with respect to angle of attack, tilt angle, airspeed, thrust of tilt-rotor and coaxial fan are defined during transitional flight. A transitional flight scenario is developed with force and moment equations.
Countermeasure Techniques for Deceptive Phishing Attack

Phishing is a form of online identity theft. Phishers use social engineering to steal victims' personal identity data and financial account credentials. Social engineering schemes use spoofed e-mails to lure unsuspecting victims into counterfeit websites designed to trick recipients into divulging financial data such as credit card numbers, account usernames, passwords and social security numbers. This is called a deceptive phishing attack. In this paper, a thorough overview of a deceptive phishing attack and its countermeasure techniques, which is called anti-phishing, is presented. Firstly, technologies used by phishers and the definition, classification and future works of deceptive phishing attacks are discussed. Following with the existing anti-phishing techniques in literatures and research-stage technologies are shown, and a thorough analysis which includes the advantages and shortcomings of countermeasures is given. At last, we show the research of why people fall for phishing attack.
DesignScape: Design with Interactive Layout Suggestions

Creating graphic designs can be challenging for novice users. This paper presents DesignScape, a system which aids the design process by making interactive layout suggestions, i.e., changes in the position, scale, and alignment of elements. The system uses two distinct but complementary types of suggestions: refinement suggestions, which improve the current layout, and brainstorming suggestions, which change the style. We investigate two interfaces for interacting with suggestions. First, we develop a suggestive interface, where suggestions are previewed and can be accepted. Second, we develop an adaptive interface where elements move automatically to improve the layout. We compare both interfaces with a baseline without suggestions, and show that for novice designers, both interfaces produce significantly better layouts, as evaluated by other novices.
AR-Based Hologram Detection on Security Documents Using a Mobile Phone

Holograms are used frequently in creating fraud resistant security documents, such as passports, ID cards or banknotes. The key contribution of this paper is a real time method to automatically detect holograms in images acquired with a standard smartphone. Following a robust algorithm for creating a tracking target, our approach evaluates an evolving stack of observations of the document to automatically determine the location and size of holograms. We demonstrate the plausibility of our method using a variety of security documents, which are common in everyday use. Finally, we show how suitable data can be captured in a novel mobile gaming experience and draw the link between serious applications and entertainment.
Condition Monitoring of Power Transformers - Bibliography Survey

Power transformers are one of the most expensive elements in a power system and their failure due to any reason is a very bad event also to maintain & rectify the problems related to insulation failure become more expensive. Power transformers are mainly involved in the energy transmission and distribution. Unplanned power transformer outages have a considerable economics impact on the operation of electric power network. To have reliable operation of transformers, it is necessary to identify problems at an early stage before a catastrophic failure occurs. In spite of corrective and predictive maintenance, preventive maintenance of power transformer is gaining due importance in the modern era and it must be taken into account to obtain the highest reliability of power apparatus such as power transformers. The well known preventive maintenance techniques such as DGA, conditioning monitoring, partial discharge measurement, effect of moisture, Paper insulation, Oil insulation, mechanical strength, thermal conductivity, copper sulphur, bubble effect, drying process, thermal degradation, fault diagnosis, etc. are performed on transformer for a specific type of problem. There is a universal requirement for up-to-date bibliographic information on insulation system of power transformer in the academic, research and engineering communities. The same topic was earlier updated in 2008 and it is observed that many new areas have been identified by the researchers such as bubble effect, copper sulphur, mechanical strength, etc. This article lists relevant references grouped according to the topics described above. The research scholars can found all the research which have been carried out till date in this paper.
Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations

We present results from an experiment similar to one performed by Packard [24], in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton’s λ parameter , and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near “critical” λ values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with λ values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to λ, dynamical-behavior classes, and computation in CA. The main constructive results of our study are identifying the emergence and competition of computational strategies and analyzing the central role of symmetries in an evolutionary system. In particular, we demonstrate how symmetry breaking can impede the evolution toward higher computational capability. Santa Fe Institute, 1660 Old Pecos Trail, Suite A, Santa Fe, New Mexico, U.S.A. 87501.  mm@santafe.edu, pth@santafe.edu Physics Department, University of California, Berkeley, CA, U.S.A. 94720.
Example Based 3D Reconstruction from Single 2D Images

We present a novel solution to the problem of depth reconstruction from a single image. Single view 3D reconstruction is an ill-posed problem. We address this problem by using an example-based synthesis approach. Our method uses a database of objects from a single class (e.g. hands, human figures) containing example patches of feasible mappings from the appearance to the depth of each object. Given an image of a novel object, we combine the known depths of patches from similar objects to produce a plausible depth estimate. This is achieved by optimizing a global target function representing the likelihood of the candidate depth. We demonstrate how the variability of 3D shapes and their poses can be handled by updating the example database on-the-fly. In addition, we show how we can employ our method for the novel task of recovering an estimate for the occluded backside of the imaged objects. Finally, we present results on a variety of object classes and a range of imaging conditions.
Training-Based Object Recognition in Cluttered 3D Point Clouds

Recognition of three dimensional (3D) objects is a challenging problem, especially in cluttered or occluded scenes. Many existing methods focus on a specific type of object or scene, or require prior segmentation. We describe a robust and efficient general purpose 3D object recognition method that combines machine learning procedures with 3D local features, without a requirement for a priori object segmentation. Experiments validate our method on various object types from engineering and street data scans.
One Trillion Edges: Graph Processing at Facebook-Scale

Analyzing large graphs provides valuable insights for social networking and web companies in content ranking and recommendations. While numerous graph processing systems have been developed and evaluated on available benchmark graphs of up to 6.6B edges, they often face significant difficulties in scaling to much larger graphs. Industry graphs can be two orders of magnitude larger hundreds of billions or up to one trillion edges. In addition to scalability challenges, real world applications often require much more complex graph processing workflows than previously evaluated. In this paper, we describe the usability, performance, and scalability improvements we made to Apache Giraph, an open-source graph processing system, in order to use it on Facebook-scale graphs of up to one trillion edges. We also describe several key extensions to the original Pregel model that make it possible to develop a broader range of production graph applications and workflows as well as improve code reuse. Finally, we report on real-world operations as well as performance characteristics of several large-scale production applications.