nils-herrmann/clean_scirepeval huggingface dataset:
RMDL: Random Multimodel Deep Learning for Classification

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.
Effects of lumbar stabilization exercise on functional disability and lumbar
lordosis angle in patients with chronic low back pain

This study examined the effects of lumbar stabilization exercises on the functional disability and lumbar lordosis angles in patients with chronic low back pain.  The subjects were 30 patients with chronic low back pain divided into a lumbar stabilization exercise group (n = 15) and a conservative treatment group (n = 15).  The lumbar stabilization exercise and conservative treatment groups performed an exercise program and conservative physical treatment, respectively. Both programs were performed 3 times a week for 6 weeks. The degree of functional disability was assessed by the Oswestry disability index, and lumbar lordosis angles were measured by plain radiography.  The Oswestry disability index decreased significantly in the both groups; however, it was significantly lower in the lumbar stabilization exercise group. The lumbar lordosis angle increased significantly in the lumbar stabilization exercise group after treatment and was also significantly greater than that in the conservative treatment group.  Lumbar stabilization exercise is more effective than conservative treatment for improving functional disability and lumbar lordosis angles.
Semi-autonomous flying robot for physical interaction with environment

This contribution presents the first results of the development of an unmanned aerial vehicle (UAV) which is capable of applying force to a wall while maintaining flight stability. This is a novel idea since UAVs are used so far only for tasks without physical contact to the surrounding objects. The basis for the work presented is a quadrotor system which is stabilized with an inertial measurement unit. As a new approach an additional actuator was added to generate forces in physical contact while the UAV stays horizontal. A control architecture based on ultrasonic distance sensors and a CMOS-camera is proposed. The performance of the system was proved by several flight tests. Potential applications of the system can be physical tasks at high places like cleaning windows or walls as well as rescue or maintenance tasks.
Emerging cognitive radio applications: A survey

Recent developments in spectrum policy and regulatory domains, notably the release of the National Broadband Plan, the publication of final rules for TV white spaces, and the ongoing proceeding for secondary use of the 2360-2400 MHz band for medical body area networks, will allow more flexible and efficient use of spectrum in the future. These important changes open up exciting opportunities for cognitive radio to enable and support a variety of emerging applications, ranging from smart grid, public safety and broadband cellular, to medical applications. This article presents a high-level view on how cognitive radio (primarily from a dynamic spectrum access perspective) would support such applications, the benefits that cognitive radio would bring, and also some challenges that are yet to be resolved. We also illustrate related standardization that uses cognitive radio technologies to support such emerging applications.
MEMS Capacitive Pressure Sensors : A Review on Recent Development and Prospective

Recently MEMS Capacitive Pressure Sensor gains more advantage over micromachined piezoresistive pressure sensor due to high sensitivity, low power consumption, free from temperature effects, IC compatibility, etc,. The spectrum of capacitive pressure sensor application is increasing, hence it is essential to review the path of technological development and further prospective of micromachined capacitive pressure sensor. This paper focuses on the review of various types of capacitive pressure sensor principle, MEMS materials used in fabrication, procedures adopted in microfabrication for silicon and polymer material diaphragm, bonding and packaging techniques used. Selected result on capacitive sensitivity, effect of temperature on capacitive sensitivity was also presented. Finally, the development of smart sensor was discussed. MEMS Capacitive pressure sensor, Review on pressure sensor, CDPS, MEMS Fabrication, MEMS Material, pressure sensor, MEMS (Micro Electro Mechanical System)
Analysis of participation in an online photo-sharing community: A multidimensional perspective

In recent years we have witnessed a significant growth of social-computing communities—online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo-sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users’ participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed.
An analysis of 3D point cloud reconstruction from light field images

Current methodologies for the generation of 3D point cloud from real world scenes rely upon a set of 2D images capturing the scene from several points of view. Novel plenoptic cameras sample the light field crossing the main camera lens creating a light field image. The information available in a plenoptic image must be processed in order to render a view or create the depth map of the scene. This paper analyses a method for the reconstruction of 3D models. The reconstruction of the model is obtained from a single image shot. Exploiting the properties of plenoptic images, a point cloud is generated and compared with a point cloud of the same object but generated with a different plenoptic camera.
Context-Aware Synthesis and Placement of Object Instances

Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem. Solving it requires (a) determining a location to place an object in the scene and (b) determining its appearance at the location. Such an object insertion model can potentially facilitate numerous image editing and scene parsing applications. In this paper, we propose an end-to-end trainable neural network for the task of inserting an object instance mask of a specified class into the semantic label map of an image. Our network consists of two generative modules where one determines where the inserted object mask should be (i.e., location and scale) and the other determines what the object mask shape (and pose) should look like. The two modules are connected together via a spatial transformation network and jointly trained. We devise a learning procedure that leverage both supervised and unsupervised data and show our model can insert an object at diverse locations with various appearances. We conduct extensive experimental validations with comparisons to strong baselines to verify the effectiveness of the proposed network.
Accurate activity recognition in a home setting

A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.
Intelligent scheduling at NASA: Application to ground operations at Kennedy Space Center

NASA's Kennedy Space Center (KSC) is working with Stottler Henke to improve KSC's Ground Processing (GP) scheduling. The solution called, Aurora-KSC, has been designed, developed and deployed at KSC to automate a large amount of Kennedy Space Center's planning, scheduling, and execution decision-making. Aurora-KSC leverages Aurora, the world's most intelligent scheduling framework; Aurora itself originated in part from many earlier NASA-funded efforts and has been utilized by NASA for some of its most complex scheduling challenges, including the scheduling of the maintenance, repair & overhaul (MRO) of the Space Shuttle during its tenure. Aurora-KSC expands on this foundation, enhancing and extending Aurora for improved generalization and adaptability. This includes an interface with Primavera P6 and Microsoft Project so project information already available in these formats can benefit from Aurora-KSC. This allows KSC to realize significant efficiency improvements in several different areas. Aurora-KSC also includes adaptive execution, that is, executing the scheduled activities/procedures intelligently as actual execution actually deviates from the original schedule. Aurora-KSC is available to the wider NASA community to provide its many benefits to the rest of NASA. Aurora-KSC is being applied to several problems at KSC relating to Ground Processing (GP). The delivered application improves the scheduling of Space Launch System (SLS) Processing and verification & validation (V&V) activity, including reduced turnaround time in response to changes and what-ifs, and more optimal schedules. This paper reviews the goals that lead to the development of Aurora-KSC, including background on the pre-existing proven technologies that were leveraged. The application to NASA challenges will be discussed with benefits to date. Finally, the benefits of leveraging Aurora-KSC for other NASA applications will be discussed.
Text Coherence Analysis Based on Deep Neural Network

In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.
Multi-linear polarization reconfigurable center-fed circular patch antenna with shorting posts

In this paper, a novel multi-linear polarization reconfigurable antenna with shorting posts, which can achieve four linear polarizations (0°, 45°, 90°, 135°), has been proposed. By switching the diodes between two groups of shorting posts, four linear polarizations can be realized. The dimensions of the proposed antenna are about 0.56λ× 0.56λ× 0.07λ at 2.4 GHz. The measured results agree well with the simulated ones.
Novel zero-Voltage-transition PWM DC-DC converters

A new family of zero-voltage-switching (ZVS) pulsewidth-modulated (PWM) converters that uses a new ZVS-PWM switch cell is presented in this paper. Except for the auxiliary switch, all active and passive semiconductor devices in the ZVS-PWM converters operate at ZVS turn ON and turn OFF. The auxiliary switch operates at zero-current-switching (ZCS) turns ON and OFF. Besides operating at constant frequency, these new converters have no overvoltage across the switches and no additional current stress on the main switch in comparison to the hard-switching converter counterpart. Auxiliary components rated at very small current are used. The principle of operation, theoretical analysis, and experimental results of the new ZVS-PWM boost converter, rated 1 kW, and operating at 80 kHz, are provided in this paper to verify the performance of this new family of converters.
An improved fruit fly optimization algorithm and its application to joint replenishment problems

Fruit fly optimization algorithm (FOA) is one of the recent evolutionary computation approaches. This paper presents an effective and improved FOA (IFOA) for optimizing numerical functions and solving joint replenishment problems (JRPs). In the proposed IFOA, a new method of maintaining the population diversity is developed to enhance the exploration ability. Fruit flies with better fitness values use vision to fly toward a new location, and the others fly randomly in initial search space based on swarm collaboration. In addition, a new parameter to avoid the acquisition of local optimal solution is introduced to implement intelligent searching. Random perturbation is added to the updated initial location to jump out of the local optimum. Comparisons are carried out using 18 benchmark functions to verify the performance of the IFOA. Experimental results show that IFOA has better comprehensive performance than the original FOA, differential evolution algorithm, and particle swarm optimization algorithm. The IFOA is also utilized to solve the typical JRPs that have been proven as non-deterministic polynomial hard problems. Comparative examples reveal that the proposed IFOA can find better solutions than the current best algorithm; thus, it is a potential tool for various complex optimization problems.
Understanding Semantic Impact of Source Code Changes : an Empirical Study

Since source code is the ultimate definition of the behavior of a software product, changes to source code become the critical factor in understanding behavioral changes and predicting faults. In studies on source code changes, text or syntactic approaches have been widely used. Textual analysis focuses on changed text fragments while syntactic analysis focuses on changed syntactic entities. Although both of them have demonstrated their advantages in experimental results, they have only focused on changed code. Because of semantic dependencies within programs, we believe that code impacted by changes is also helpful. Given a source code change, we identify its impact by program slicing along the variable def-use chains. To evaluate the effectiveness of change impacts in fault detection and prediction, we compare impacted code with changed code according to size and fault density. Our experiment on the change history of a successful industrial project shows that: for large changes, their impacts have relative small size and high fault density; while for small changes, change themselves have relative small size and high fault density. Our study suggests that: 1) change impacts are complementary to change themselves in detecting or predicting faults; 2) within the impact of a large change, a high degree of interference between impacts of different changed lines contributes to the high fault density; 3) high fault density in impacts aggravates the danger of large
A 5.58 nW Crystal Oscillator Using Pulsed Driver for Real-Time Clocks

A 5.58 nW real-time clock using a crystal oscillator is presented. In this circuit, the amplifier used in a traditional circuit is replaced with pulsed drivers. The pulse is generated with precise timing using a DLL. With this approach, an extremely low oscillation amplitude of 160 mV results in low-power operation. This low-amplitude oscillation is sustained robustly using additional supply voltages: a lower supply for the final drive stage and a higher supply used for pulses that drive the final drive stage, which ensures low ON-resistance necessary for reliable operation. The different supply levels are generated on-chip by a switched capacitor network (SCN) from a single supply. The circuit has been tested at different supply voltages and temperatures. It shows a minimum power consumption of 5.58 nW and power supply sensitivity of 30.3 ppm/V over supply voltage of 0.94-1.2 V, without degrading the crystal's temperature dependency: between -20 °C and 80 °C. Moreover, its performance as a real-time clock has been verified by measurement of an Allan deviation of 1.16 × 10-8.
1 Pavement distress detection methods : a review 2

The road pavement condition affects safety and comfort, traffic and travel times, vehicles 8 operating cost and emission levels. In order to optimize the road pavement management and 9 guarantee satisfactory mobility conditions for all the road users, the Pavement Management 10 System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of 11 pavement distress data, the possibility of data maintenance and updating, in order to evaluate the 12 best maintenance program. In the last decade, many researches have been focused on pavement 13 distress detection, using a huge variety of technological solutions for both data collection and 14 information extraction and qualification. This paper presents a literature review of data collection 15 systems and processing approach aimed at the pavement condition evaluation. Both commercial 16 solutions and research approaches have been included. The main goal is to draw a framework of 17 the actual existing solutions, considering them from a different point of view in order to identify 18 the most suitable for further research and technical improvement, also considering the automated 19 and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the 20 data collection and extraction to the type of distress, considering the distress detection, 21 classification and quantification phases of the procedure.
Pose Guided Human Video Generation

Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a Pose Sequence Generative Adversarial Network (PSGAN) learns in an adversarial manner to yield pose sequences conditioned on the class label. In the second stage, a Semantic Consistent Generative Adversarial Network (SCGAN) generates video frames from the poses while preserving coherent appearances in the input image. By enforcing semantic consistency between the generated and ground-truth poses at a high feature level, our SCGAN is robust to noisy or abnormal poses. Extensive experiments on both human action and human face datasets manifest the superiority of the proposed method over other state-of-the-arts.
Broad-Band High-Power Amplifier Using Spatial Power-Combining Technique

High power, broad bandwidth, high linearity, and low noise are among the most important features in amplifier design. The broad-band spatial power-combining technique addresses all these issues by combining the output power of a large quantity of microwave monolithic integrated circuit (MMIC) amplifiers in a broad-band coaxial waveguide environment, while maintaining good linearity and improving phase noise of the MMIC amplifiers. A coaxial waveguide was used as the host of the combining circuits for broader bandwidth and better uniformity by equally distributing the input power to each element. A new compact coaxial combiner with much smaller size is investigated. Broad-band slotline to microstrip-line transition is integrated for better compatibility with commercial MMIC amplifiers. Thermal simulations are performed and an improved thermal management scheme over previous designs is employed to improve the heat sinking in high-power application. A high-power amplifier using the compact combiner design is built and demonstrated to have a bandwidth from 6 to 17 GHz with 44-W maximum output power. Linearity measurement has shown a high third-order intercept point of 52 dBm. Analysis shows the amplifier has the ability to extend spurious-free dynamic range by 2 3 times. The amplifier also has shown a residual phase floor close to 140 dBc at 10-kHz offset from the carrier with 5–6-dB reductions compared to a single MMIC amplifier it integrates.
Named Entity Recognition and Compositional Morphology for Word Representations in Chinese

We approach the task of named entity recognition (NER) for Chinese by representing each entity with a composition of its traits: the token-entity, its characters, and its characters’ main radicals. Character and radical-level information for each entity are included to provide additional relationships that might not be strictly captured within a token-entity’s word embedding during training. We learn using neural networks that are some combination of the following traits: unidirectional or bidirectional; single or multi-layer; simple, gated recurrent unit (GRU), or long short term memory (LSTM) celled. We achieve a maximum not-O token-level F1 score of 76 and entity-level F1 of 70.
Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network

Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.