nils-herrmann/clean_scirepeval huggingface dataset:
On Arbitrating the Power-Performance Tradeoff in SaaS Clouds

In this paper, we present an analytical framework for characterizing and optimizing the power-performance tradeoff in Software-as-a-Service (SaaS) cloud platforms. Our objectives are two-folded: 1) We maximize the operating revenue when serving heterogeneous SaaS applications with unpredictable user requests. 2) We minimize the power consumption when processing the user requests. To achieve these objectives, we construct a unified profit-maximizing objective to jointly consider revenue and cost in an economic view. An offline solution to maximize the supreme bound of the objective is first developed, to 1) justify the validity of our theoretical model, and 2) establish a benchmark to examine the effectiveness of other control solutions. As a highlight of our contributions, we take advantage of the Lyapunov optimization techniques to design and analyze an optimal yet practical control framework, which makes online decisions on request admission control, routing, and virtual machine (VMs) scheduling. Our control framework can accommodate a variety of design choices and operational requirements in a datacenter. Specifically, buffering facilities can be introduced to alleviate the bursty admitted requests and to improve the robustness of the system, and a power budget can be enforced to improve the datacenter performance (dollar) per watt. Our mathematical analyses and simulations have demonstrated both the optimality (in terms of the cost-effective power-performance tradeoff) and stability (in terms of robustness and adaptivity to time-varying and bursty user requests) achieved by our proposed control framework.
Enhancement imaging on multi-path signals in ultra-wideband radar

This paper develops a method for enhancement imaging of objects when there are reflecting planes around in the environment. When the location of these planes is known, we can exploit the multipath signals reflected by them in imaging process. Coherent imaging algorithm, like backprojection (BP) algorithm, is typically achieved by direct returns. In this paper, a modified backprojection (MBP) coherent imaging algorithm is proposed to improve the image quality of interested targets by exploiting the multipath signals. The wave reflection analysis shows that, in order to obtain optimum resolution, the multipath signals should be processed before being used to imaging. The key points lie on the compensation of phase mutation and amplitude attenuation. After this process, the resolution of targets has a significant improvement. Simulations are used to validate the proposed algorithms. (4 pages)
Design of New Type License Plates Based on RFID and its Secure Automatic Identification System

In order to overcome the shortages of visibility, uniqueness and intelligence existing in traditional vehicle license plate, we propose a new type design of license plate and develop a matched secure automatic identification system. We adopt new accumulating energy self-luminous material and RFID technology in the system, moreover secrete starting address and random number initialization are implemented to ensure data security, as greatly improve the visibility, uniqueness and intelligence of license plate. Through repetitious experiments and long-running stable status of system, we have proved the feasibility and security of the system.
TiN coating/glass substrate system fabricated for hot-embossing stamp at multi-scale

For the hot-embossing lithography, imprinting stamp with long-life span, good anti-wear property and precise geometrical shape, is much expected for pushing forward the technology to industrial application. By analyzing disadvantages of current Si and SiO2 imprinting stamps, this paper presents TiN coating/glass substrate system as the stamp material, in which the glass plates serves as substrate and the hard TiN coating is fabricated for the nano-patterns. To fabricate the stamp, firstly, several microns TiN coating is deposited on the glass by ion- beam deposition system, then focused ion beam etching system is used to fabricate a series of nano-patterns on the TiN coating. The primary hot-embossing imprinting results indicate good results for PMMA. Hereby it is believed that conventional hard coating TiN could be potentially a good choice for realizing the long-life imprinting and improving the life duration of the imprinting stamp greatly.
Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees

This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The SENC problem can be decomposed into three subproblems: detecting emerging new classes, classifying known classes, and updating models to integrate each new class as part of known classes. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The proposed method employs completely-random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. The completely-random trees are used as a single common core to solve all three subproblems: unsupervised learning, supervised learning, and model update on data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods.
Erasing false-location of two stations direction-finding cross location in multi-path and multiple sources environments

Among methods of passive location to get positional information of radio sources, direction-finding cross location is convenient and often used. However, it has false-location in environments of multipath and multiple sources. This paper uses the technology of array direction-finding and adaptive beamforming for signal separation and recognition, based on signal correlation characters. All false-locations are erased successfully. Computer simulations show that this method is effective.
A Temperature Self-Compensated LPFG Sensor for Large Strain Measurements at High Temperature

In this paper, a CO2 laser-induced long-period fiber-grating (LPFG) optic sensor was packaged with a hybrid mechanism of elastic attachment and gauge length change for large strain measurements in a high-temperature environment. An emphasis was placed on the use of two cladding modes (LP06 and LP07) of a single LPFG sensor for simultaneous strain and temperature evaluations so that exact temperature was used to compensate strain measurements. Both strain and temperature sensitivities of the LPFG sensor, as well as the strain transfer ratio due to a combined effect of elastic attachment and gauge length change, were analytically derived and validated with tension tests at elevated temperatures. The strain sensitivity of the LPFG sensor switched sign from negative for LP06 or lower modes to positive for LP07 or higher modes, whereas its temperature sensitivity remained positive. The sign switch for the strain sensitivity resulted from two competing changes of grating period and effective refractive index as the gratings are subjected to an axial strain. The LPFG sensor was demonstrated to be operational up to 700°C for a strain measurement of up to 1.5%.
SPC12-3: Cumulant-Based JAFE Algorithm in Spatially and Temporally Correlated Gaussian Noise

A cumulant-based joint angle-frequency estimation (CB-JAFE) algorithm for angle-frequency parameter estimation of independent nonGaussian sources is proposed. The proposed CB-JAFE algorithm, which utilizes the fourth-order cumulants of the spatio-temporal data, can effectively suppress the spatially and temporally correlated Gaussian noise with unknown correlation structure. Simulation results are carried out to demonstrate the effectiveness of the proposed algorithm compared with the JAFE algorithm.
Invariant Topology Snakes Driven by Particle Swarm Optimizer

Active contour modelling (ACM) plays a significant role in boundaries extraction or image segmentation. In this paper, we present a method of invariant topology snakes driven by particle swarm optimizer (ITPSO snakes) and its application in ACM. We use some control points on the contour as particles in ITPSO. To avoid intersections and maintain topology of the active contour in the process of ACM, and consider inhomogeneous boundaries, we design a novel scheme to restrict the velocity updates. So the best discovered positions for next round velocity updates are no more computed as in the traditional method of PSO. The experimental results show that the proposed ITPSO snakes can perform well in homogeneous boundaries extraction.
Wind Power Trading Under Uncertainty in LMP Markets

This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.
A Coupled Memcapacitor Emulator-Based Relaxation Oscillator

This brief proposes a floating emulator circuit for mimicking the dynamic behaviors of flux coupled memcapacitors (MCs) by making use of common off-the-shelf active devices. The equivalent constitutive relation of the coupled MC emulators is theoretically presented in details. The coupled MCs are then utilized to structure new relaxation oscillators (ROs) with the purpose of achieving controllable oscillation frequency and duty cycle. In consideration of different parameter configuration cases, an experimental investigation is carried out using a practical circuit emulating the dual coupled MCs and the ROs. Good agreement between experimental and theoretical results confirms that dual coupled MCs can be utilized to structure ROs with controllability.
Research and implementation of a novel single phase AC/AC variable frequency technology

A novel single phase AC/AC variable frequency technology is presented, which is based on plugged pulse-chopping control scheme in this paper. The single-phase circuit topology is given, and the principle and control strategies are detailed. Compared with the traditional AC/AC variable frequency technology, its structure and control are simpler and cost is lower. Simulation under the PSPICE environment is done. The hardware circuit based on FPGA is designed and the software implementation by VHDL language is completed. The simulation results are the same as experimental results, which are verified to be reasonable and correct.
A Taxi Driving Fraud Detection System

Advances in GPS tracking technology have enabled us to install GPS tracking devices in city taxis to collect a large amount of GPS traces under operational time constraints. These GPS traces provide unparallel opportunities for us to uncover taxi driving fraud activities. In this paper, we develop a taxi driving fraud detection system, which is able to systematically investigate taxi driving fraud. In this system, we first provide functions to find two aspects of evidences: travel route evidence and driving distance evidence. Furthermore, a third function is designed to combine the two aspects of evidences based on Dempster-Shafer theory. To implement the system, we first identify interesting sites from a large amount of taxi GPS logs. Then, we propose a parameter-free method to mine the travel route evidences. Also, we introduce route mark to represent a typical driving path from an interesting site to another one. Based on route mark, we exploit a generative statistical model to characterize the distribution of driving distance and identify the driving distance evidences. Finally, we evaluate the taxi driving fraud detection system with large scale real-world taxi GPS logs. In the experiments, we uncover some regularity of driving fraud activities and investigate the motivation of drivers to commit a driving fraud by analyzing the produced taxi fraud data.
Hidden behavior prediction of complex systems based on semi-quantitative information

In engineering, it is important to predict hidden behaviors of complex systems. The existing methods for predicting the hidden behavior cannot use semi-quantitative information. As such, in this paper a new BRB based model is proposed to predict the hidden behavior. In the proposed BRB based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood (ML) algorithm. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm.
A novel method for simultaneous gesture segmentation and recognition based on HMM

Gesture recognition is a big area of artificial intelligence, gesture segmentation is the difficult problem of continuous vocabulary gesture recognition. There are many automatic techniques to segment gesture, however, most of them have an time interval between the gesture segmentation and output recognition results. The interval is not great for performance of continuous gesture recognition. In order to avoid the time interval, a novel method of continuous vocabulary gesture recognition is proposed. In our method, the start point and the end position of every gesture sequence are found by judging the change of the probability. The probability is the probability of gesture sequence occurrence that is defined by the gesture sequence in the Hidden Markov Model (HMM). We also propose a method to automatically determine the threshold used in the algorithm, which can effectively improve the segmentation accuracy and make the algorithm having better robustness. In the experiment, 93.88 % accuracy can be obtained to the gesture segmentation and 92.22 % accuracy can be obtained to the gesture recognition after segmented.
The analysis of UWB SAR system error

Focusing on the design of Ultra WideBand (UWB) Synthetic Aperture Radar (SAR) adopting deramping technology, the paper has studied the sources of the system error and the error tolerable levels. The system error classification is introduced according to the error's characteristics. The effect of each kind of error on UWB SAR system is presented. The paper has allocated the error budget with a UWB SAR system which has about 200 Mhz bandwidth and works at UHF/VHF band.
Subaperture wiener filter construction for time-varying RFI suppression in UWB SAR

Ultra-WideBand synthetic aperture radar (UWB SAR), operating in the very high frequency/ultra-high frequency bands, has foliage and ground penetrating capability to detect targets hidden in forest or under the ground. The existing services such as television, radio and communications, referred to as radio frequency interference (RFI), degrade the quality of focused UWB SAR image. The Wiener filter has been found to have superior suppression performance and be more computationally efficient than other RFI suppression methods but its construction needs additional recorded RFI data, which increases the radar system complexity. In this paper, a subaperture Wiener filter (SWF) construction method is proposed, which does not require additional recorded RFI data and can deal with the time-varying RFI. The proposed SWF has been proved efficient by real UWB SAR data.
Geometric modeling and control for the full-actuated aerial manipulating system

This paper proposes a geometric control scheme to stabilize the full-actuated mobile manipulating unmanned aerial system (MM-UAV) to a desired configuration. For this purpose, we firstly describe the kinematics and dynamics of the full-actuated mobile manipulating unmanned aerial system by incorporating the standard concepts from Lie group and Lie algebra. Then, a controller with a proportional derivative (PD) linear action and a nonlinear gravity compensating action is designed by introducing a Morse function. Finally, a number example is presented to demonstrate the effectiveness of the controller.
AC-AC Conversion with Carrier Frequency of Electric Network And Application Prospect

This paper proposes a new principle of variable frequency for AC-AC conversion, which operates below the half-frequencies of electric network like traditional cycloconverters, but very different from it in scheme. The new variable frequency principle is described by means of a well-known concept of amplitude modulation from microelectronics, and the waveform control mode adopts chopper scheme. Experiments show the new conversion principle making progress than traditional cycloconverters in high power factor, less power device number and simple control, so getting same or better performances at low cost. The application prospect of the new principle is also described particularly for buffering energy shortage problem.
Analyzing Evolutionary Optimization in Noisy Environments

Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoretical analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better.
Metamorphic Testing for Software Quality Assessment: A Study of Search Engines

Metamorphic testing is a testing technique that can be used to verify the functional correctness of software in the absence of an ideal oracle. This paper extends metamorphic testing into a user-oriented approach to software verification, validation, and quality assessment, and conducts large scale empirical studies with four major web search engines: Google, Bing, Chinese Bing, and Baidu. These search engines are very difficult to test and assess using conventional approaches owing to the lack of an objective and generally recognized oracle. The results are useful for both search engine developers and users, and demonstrate that our approach can effectively alleviate the oracle problem and challenges surrounding a lack of specifications when verifying, validating, and evaluating large and complex software systems.
Object detection and tracking based on adaptive canny operator and GM(1,1) model

A new method of moving object detection and tracking from dynamical image sequence is proposed in this paper. This method uses information entropy to achieve the adaptive selection of high threshold in Canny operator. The modified Canny operator is used to detect the object contour from difference images. And this detected contour is then used as the template of partial Hausdorff distance matching. This method predicts the position of moving object in next frame by improved grey prediction model GM(1,1), so it can enhance matching efficiency by taking such position as reference point to locate the search region. The experimental results show that compared with alpha-beta-gamma filter, the grey prediction model GM(1,1) can make error smaller and work stably under information-limited situation, so the grey prediction trajectory can reflect the trend of moving object more accurately and is closer to the actual motion trajectory.
Active Learning by Querying Informative and Representative Examples

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.
A general design technique for fault diagnostic systems

We put forward a design method for fault diagnostic systems (FDSs) by proposing a fault model and using the incremental hybrid learning algorithm which tightly combines symbolic learning and neural networks. It is capable of overcoming several shortcomings in existing diagnostic systems, such as the lack of universality, the unbalance in the use of fault prior knowledge and the dynamic data and the dilemma of stability and plasticity. Experiment showed the FDS implemented by this kind of method had a good diagnostic ability.