nils-herrmann/clean_scirepeval huggingface dataset:
Pixel-based airplanes segmentation in remote sensing image

In this paper, we present a novel pixel-based airplane segmentation method from remote sensing images by combining Single Shot MultiBox Detector (SSD) and Single-layer Cellular Automata (SCA). SSD is a kind of deep ConvNet for object detection while SCA is a saliency detection method via Cellular Automata. First, we obtain detection result where every airplane is boxed by a rectangle through the SSD model. The last two conventional layers in original SSD are removed in order to fit the small objects of remote sensing (RS) image. Then the result is processed via single-layer Cellular Automata to achieve pixel-based segmentation. The experiments demonstrate that our approach is efficient and works well for automatic airplane segmentation in RS image.
Social Learning

In recent years, social behavioral data have been exponentially expanding due to the tremendous success of various outlets on the social Web (aka Web 2.0) such as Facebook, Digg, Twitter, Wikipedia, and Delicious. As a result, there's a need for social learning to support the discovery, analysis, and modeling of human social behavioral data. The goal is to discover social intelligence, which encompasses a spectrum of knowledge that characterizes human interaction, communication, and collaborations. The social Web has thus become a fertile ground for machine learning and data mining research. This special issue gathers the state-of-the-art research in social learning and is devoted to exhibiting some of the best representative works in this area.
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backpropagation for multilabel learning, is proposed. It is derived from the popular backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms
A postfilter method for refraction and dispersion effects compensation in GPSAR image

Refraction and dispersion make the subsurface targets defocus in ground penetrating SAR (GPSAR). In this paper, a postfilter is proposed to compensate the refraction and dispersion effects, which is more computationally efficient than the previously proposed prefilter method. The postfilter method is tested by the field data of the rail-GPSAR system to yield satisfactory results
Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then, we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a two-settlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.
Interactive exemplar-based segmentation toolkit for biomedical image analysis

In the field of biomedical imaging analysis on single-cell level, reliable and fast segmentation of the cell nuclei from the background on three-dimensional images is highly needed for the further analysis. In this work we propose an interactive cell segmentation toolkit that first establishes a set of exemplar regions from user input through an easy and intuitive interface in both 2D and 3D in real-time, then extracts the shape and intensity features from those exemplars. Based on a local contrast-constrained region growing scheme, each connected component in the whole image would be filtered by the features from exemplars, forming an “exemplar-matching” group which passed the filtering and would be part of the final segmentation result, and a “non-exemplar-matching” group in which components would be further segmented by the gradient vector field based algorithm. The results of the filtering process are visualized back to the user in near real-time, thus enhancing the experience in exemplar selecting and parameter tuning. The toolkit is distributed as a plugin within the open source Vaa3D system (http://vaa3d.org).
The Design of Miniaturized Printed Wire Antennas Using Double-Layer Periodic Metallization

The design of miniaturized printed wire antennas using multilayer vias loaded periodic metallization is presented. The proposed structure is evolved from a periodic slow-wave transmission line, where each unit cell is made of a patch and two loops spread over two metal layers connected in series through vias. A section of the periodic metallization is used as a quarter-wave monopole antenna protruded over a truncated ground plane and fed by a microstrip line. The design prototype at 2.4 GHz band shows that it is possible to obtain 24% 10 dB bandwidth with 80% efficiency at an antenna length of 12 mm long without tuning inductors, more than a 50% reduction from a normal printed straight monopole.
A Rail-mounted UWB SAR Landmines Detection System --- Rail-GPSAR

A standoff landmine detection system, rail-GPSAR, was built for the purposes to evaluate feasibility and effectiveness of landmine detection using UWB SAR. The system architecture of rail-GPSAR is introduced firstly, and then some new approaches to RFI/coupling suppression; near-field SAR imaging and detection of the buried landmines are proposed. Finally the detection performance of rail-GPSAR is validated by a series of field experiments
Metamorphic testing and beyond

When testing a program, correctly executed test cases are seldom explored further, even though they may carry useful information. Metamorphic testing proposes to generate follow-up test cases to check important properties of the target function. It does not need a human oracle for output prediction and comparison. In this paper, we highlight the basic concepts of metamorphic testing and some interesting extensions in the areas of program testing, proving, and debugging. Future research directions are also proposed.
Fuel Cell Generation in Geo-Distributed Cloud Services: A Quantitative Study

The demand for capping carbon emission has promoted the use of fuel cell energy in cloud computing, yet it is unclear what and how much benefit it may bring. This paper, for the first time, attempts to quantitatively examine the benefits brought by fuel cell generation, and to illustrate how such benefits can be realized with an intelligent coordination between grid power and fuel cell generation. Specifically, we propose UFC, a quantitative index called the utility of the cloud using fuel cells, which captures the level of the data enters operator's overall satisfaction from energy cost, carbon emission, and workload performance. We formulate the UFC maximization problem to jointly optimize both fuel cell generation and geographical request routing. In order to avoid centralized solutions with high complexity and low scalability, we develop a distributed algorithm blending the advantages of Alternating Direction Method of Multipliers (ADMM) and the auxiliary variable method, whose performance is evaluated and verified through our extensive simulations based on real-world data enter workload traces, electricity prices and generation data sets.
Bioinformatic Analysis of a Suppression Subtractive cDNA Library from the Male Gametophytes of Laminaria japonica Aresch.

Substantial differences occur between the male and female gametophytes of Laminaria japonica Aresch in morphology and physiology. The differences were investigated by screening a cDNA library generated by suppression subtractive hybridization (SSH). Using bioinformatics and northern blot analysis, we confirmed that two genes, lhcf6 and fcp, were preferentially expressed in male gametophytes. Analyses of the randomly selected 627 clones generated by this cDNA library showed that 9 of them were unreadable due to low quality. The remainders were assembled into 187 non-redundant sequences (NRSs), of which 93 Contigs were from 524 cDNAs, showing that the redundancy rate was 69.74%. Among the 187 NRSs, 60 NRSs matched homologous proteins in public database while 122 NRSs had no significant similarity regardless of 5 NRSs due to their contamination of rRNA genes. Many of the NRSs were involved in growth and development (13.3%) and metabolism of energy and substances (26.7%) of gametophytes. In the case of metabolism, 106 cDNAs are components of the light-harvesting complex proteins, in which lhcf6 was composed of 62 NRSs and fcp was of 12 NRSs. These results showed that SSH was a useful method for discovering the differentially expressed genes between male and female gametophytes of L. japonica and for functional genomics research in this kelp. Laminaria japonica Aresch., gametophytes, SSH, cDNA library, differentially expressed gene
Generalized Low-Rank Approximations of Matrices Revisited

Compared to singular value decomposition (SVD), generalized low-rank approximations of matrices (GLRAM) can consume less computation time, obtain higher compression ratio, and yield competitive classification performance. GLRAM has been successfully applied to applications such as image compression and retrieval, and quite a few extensions have been successively proposed. However, in literature, some basic properties and crucial problems with regard to GLRAM have not been explored or solved yet. For this sake, we revisit GLRAM in this paper. First, we reveal such a close relationship between GLRAM and SVD that GLRAM's objective function is identical to SVD's objective function except the imposed constraints. Second, we derive a lower bound of GLRAM's objective function, and discuss when the lower bound can be touched. Moreover, from the viewpoint of minimizing the lower bound, we answer one open problem raised by Ye (Machine Learning, 2005), i.e., a theoretical justification of the experimental phenomenon that, under given number of reduced dimension, the lowest reconstruction error is obtained when the left and right transformations have equal number of columns. Third, we explore when and why GLRAM can perform well in terms of compression, which is a fundamental problem concerning the usability of GLRAM.
Application study on fuzzy influence diagram in highway tunnel construction safety risk assessment

In highway tunnel engineering construction activities, there are a lot of uncertain factors, and the mutual interactions of kinds of risk factors are very complex. How to comprehensively analyze and evaluate safety risk of tunnel engineering construction and plan for risk management are long-term to solve the problems in tunnel engineering safety management. Based on the influence diagram theory, this paper establishes quantitative analysis model of fuzzy influence diagram which characterizes highway tunnel construction safety risk with baihuashan tunnel project as the background, and use the risk evaluation model of fuzzy influence diagram to evaluate tunnel engineering construction safety risk. The result shows that this method is scientific and effective.
Function Substitution: Towards Constraint Solving in Software Testing

Constraint solving is a fundamental tenet of most test case generation strategies. However, conventional constraint solving methods were not designed in consideration of that aspect of test case generation. It has led to a situation where test case generation techniques can miss test cases when constraints contain function invocations. This is due to the fact that, when solving constraints to generate test cases, function invocations are not effectively handled and, as a result, solutions to some constraints are not found even though they do exist. This problem occurs in both specification-based and code-based testing. To address the problem, this paper presents a function substitution method that transforms test case constraints that contain certain types of functions into equivalent constraints that do not contain those functions. The transformation preserves the solution space and avoids missed test cases. As a result, the completeness of test case generation can be improved.
Socially-Motivated Cooperative Mobile Edge Computing

In this article we propose a novel paradigm of socially-motivated cooperative mobile edge computing, where the social tie structure among mobile and wearable device users is leveraged for achieving effective and trustworthy cooperation for collaborative computation task executions. We envision that a combination of local device computation and networked resource sharing empowers the devices with multiple flexible task execution approaches, including local mobile execution, D2D offloaded execution, direct cloud offloaded execution, and D2D-assisted cloud offloaded execution. Specifically, we propose a system model for cooperative mobile edge computing where a device social graph model is developed to capture the social relationship among the devices. We then devise a socially- aware bipartite matching based cooperative task offloading algorithm by integrating the social tie structure into the device computation and network resource sharing process. We evaluate the performance of socially-motivated cooperative mobile edge computing using both Erdos-Renyi and real-trace based social graphs, which corroborates the superior performance of the proposed socially-aware mechanism.
Testing of large number multiplication functions in cryptographic systems

Integer multiplication is one of the fundamental functions in cryptographic systems. Although much research has already been done on the testing of multiplication functions, most does not meet the need of cryptographic systems, where very large numbers are involved. Others provide only probabilistic algorithms. In this paper we propose an efficient deterministic algorithm for verifying large number multiplications in cryptographic systems. A deterministic oracle for large integer multiplication functions will result. In addition, our method can also be used to verify selected segments of digits in the product of two numbers.
Face Image Modeling by Multilinear Subspace Analysis With Missing Values

Multilinear subspace analysis (MSA) is a promising methodology for pattern-recognition problems due to its ability in decomposing the data formed from the interaction of multiple factors. The MSA requires a large training set, which is well organized in a single tensor, which consists of data samples with all possible combinations of the contributory factors. However, such a “complete” training set is difficult (or impossible) to obtain in many real applications. The missing-value problem is therefore crucial to the practicality of the MSA but has been hardly investigated up to present. To solve the problem, this paper proposes an algorithm named M2SA, which is advantageous in real applications due to the following: 1) it inherits the ability of the MSA to decompose the interlaced semantic factors; 2) it does not depend on any assumptions on the data distribution; and 3) it can deal with a high percentage of missing values. M2SA is evaluated by face image modeling on two typical multifactorial applications, i.e., face recognition and facial age estimation. Experimental results show the effectiveness of M2 SA even when the majority of the values in the training tensor are missing.
Asynchronous Field Geological Map System Based on Ajax

Ajax is one of the core technologies of RIA, which implements the asynchronous interaction between the browser and the Web server. In traditional Field Geological Map System of B/S mode, the disadvantages such as low visiting speed and bad security of such kind of Field Geological Map System have emerged. To solve the problem of low visiting speed caused by the large-volume data of filed geological map transmitted on network, the author applies the technology of Ajax to the Field Geological Map System and implements the asynchronous interaction between the browser and the Web map server. Compared with the system of B/S synchronous mode, the system avoids putting a computational burden on the browser and keeps the response time shorter to provide a higher level of interaction. In addition, the introduction of the concepts of role and privilege improves the security of the filed geological map information greatly. RIA; Field Geological Map System; Ajax; Asynchronous
Coarse-to-Fine Boundary Location With a SOM-Like Method

A coarse-to-fine boundary location with a self-organizing map (SOM)-like method is proposed in this paper. Inspired from the conventional SOM and universal gravitation, given a small quantity of supervision seeds from the desired boundaries, neurons are used to evolve to the desired boundaries in a coarse-to-fine framework. The major components of this framework are the designs of union action and evolving rate. In the course of neuron evolution, the union actions acting on these neurons will offer them the evolving directions. Also controlled by the corresponding referenced gradients, the neurons' evolving rates are adaptively adjusted at different positions. With the union actions and evolving rates, the neurons will evolve with appropriate manners to expand the set of feature points on the desired boundaries. The newly expanded feature points will cause the generation updates for feature points and neurons, and offer new information to guide the new generation of neurons to the boundaries. What is more, the proposed multiround evolution is as well a coarse-to-fine way for boundary location. Experiments and comparisons show that the proposed method performs well in complex long concavities, inhomogeneous and weak boundary location with good initialization flexibility.
Online Updating Belief-Rule-Base Using the RIMER Approach

In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis.
The Application of Stiffness-Internal Force Adjustment Method in Widening and Reinforcement of Old Highway Bridges

Stiffness-internal force adjustment is a method which is applied in widening and reinforcement of old highway bridges. This method is that two new beams with larger rigidity are respectively built on both sides of the old bridge in order to make the old bridge unloading through the stiffness-internal force adjustment. This me- thod was successfully used for the reinforcement of Yingjing Bridge. Based on calculation by the SAP, the bending moment and shearing force of the bridge's side beams reduced more than 40% under the live load and the bending moment reduced about 30% under the most unfavorable load. The load test after reinforcement showed that everything of Yingjing Bridge is normal, without traffic interruption during the construction period, and the cost of reinforcement was only 44% of the conventional reinforcement's. This method was also succ- essfully used for the reinforcement of Guanlanhe Bridge. Based on calculation by ANSYS, the maximum stress of the old beam's top and bottom unloaded more than 50% and the overall stress of the old beam unloaded more than 20% under the most unfavorable load. The calculation also showed that the maximum crack width of whole bridge decreased to 0.06 mm, the traffic wasn't interrupted during the construction period and the economic benefit was high. These achievements provide some references for widening and reinforcement of this kind of bridge in the future.
AC-SPWM-cycloconverter based on an extended chopper scheme

A novel cycloconverter based on chopper scheme is proposed, which is entirely different from the former cycloconverters based on phase-shift control, so leading to higher power factor than that of the latter, also in price of minimum power device number. Two kinds methods are derived for determining the relationships between Duty-Cycle and other parameters by means of different Area-Equivalent criteria. The single and 3-phase topologies and the ac-SPWM control strategy are introduced, which can realize variable frequency and variable voltage operation at the same time. Also some key simulations and experimental results show the validity of the conversion as well as the applicable prospect of the new cycloconverter-category.
Time delay estimation of UAV-mounted UWB SAR

In high resolution Synthetic Aperture Radar (SAR) imaging, the problem of time delay estimation emerges. Generally, the time delays can be divided into system delay and time delays between the sensor data and SAR echo. The sensors include Real Time Kinematic Global Position System (RTK GPS) and Inertial Navigation System (INS). It is computationally very expensive to search for the optimum solution in three dimensions. In this paper, the authors find out that the INS delay is less affected and can be neglected in practice, and the system delay and RTK GPS delay are relatively independent. System delay will only lead to the shift of targets' position along range direction and RTK GPS delay will usually lead to the shifted position along cross-range direction and tiny shift along range direction. Based on the above conclusions, the complicated problem of three-dimensional search is simplified to be two one-dimensional searches. An off-line delay-estimated approach based on the prepared point targets is proposed, in which we search for the time delays by aligning the position of targets in SAR image with the real position of targets measured by RTK GPS. The experimental results demonstrate the effectiveness of the proposed approach.