nils-herrmann/clean_scirepeval huggingface dataset:
Functional decomposition of Unmanned Aircraft Systems (UAS) for CNS capabilities in NAS integration

This paper presents the approach developed for the partial MASPS level document DO-344 “Operational and Functional Requirements and Safety Objectives” for the UAS standards. Previous RTCA1 work led to the production of an Operational Services Environment Description document, from which operational requirements were extracted and refined. Following the principles described in the Department of Defense Architecture Framework, the overall UAS architecture and major interfaces were defined. Interacting elements included the unmanned aircraft (airborne component), the ground control station (ground component), the Air Traffic Control (ATC), the Air Traffic Service besides ATC, other traffic in the NAS, and the UAS ground support. Furthering the level of details, a functional decomposition was produced prior to the allocation onto the UAS architecture. These functions cover domains including communication, control, navigation, surveillance, and health monitoring. The communication function addressed all elements in the UAS connected with external interfaces: the airborne component, the ground component, the ATC, the other traffic and the ground support. The control function addressed the interface between the ground control station and the unmanned aircraft for the purpose of flying in the NAS. The navigation function covered the capability to determine and fly a trajectory using conventional and satellite based navigation means. The surveillance function addressed the capability to detect and avoid collisions with hazards, including other traffic, terrain and obstacles, and weather. Finally, the health monitoring function addressed the capability to oversee UAS systems, probe for their status and feedback issues related to degradation or loss of performance. An additional function denoted `manage' was added to the functional decomposition to complement the heath monitoring coverage and included manual modes for the operation of the UAS.
Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges

In recent years, the capabilities and roles of Unmanned Aerial Vehicles (UAVs) have rapidly evolved, and their usage in military and civilian areas is extremely popular as a result of the advances in technology of robotic systems such as processors, sensors, communications, and networking technologies. While this technology is progressing, development and maintenance costs of UAVs are decreasing relatively. The focus is changing from use of one large UAV to use of multiple UAVs, which are integrated into teams that can coordinate to achieve high-level goals. This level of coordination requires new networking models that can be set up on highly mobile nodes such as UAVs in the fleet. Such networking models allow any two nodes to communicate directly if they are in the communication range, or indirectly through a number of relay nodes such as UAVs. Setting up an ad-hoc network between flying UAVs is a challenging issue, and requirements can differ from traditional networks, Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) in terms of node mobility, connectivity, message routing, service quality, application areas, etc. This paper O. K. Sahingoz (B) Computer Engineering Department, Turkish Air Force Academy, Yesilyurt, Istanbul, 34149, Turkey e-mail: sahingoz@hho.edu.tr identifies the challenges with using UAVs as relay nodes in an ad-hoc manner, introduces network models of UAVs, and depicts open research issues with analyzing opportunities and future work.
Paleolimnological reconstruction of the effects of atmospheric deposition of acids and heavy metals on the chemistry and biology of lakes in New England and Norway

Sediment cores from nine lakes in southern Norway (N) and six in northern New England (NE) were dated by 137Cs, 210Pb and in NE also by pollen, and were analyzed geochemically and for diatoms. Cores from two N and three NE lakes were analyzed for cladocerans. 137Cs dating is unreliable in these lakes, probably due to mobility of Cs in the sediment. In Holmvatn sediment, an up-core increase in Fe, starting ca. 1900, correlates with geochemical indications of decreasing mechanical erosion of soils. Diatoms indicate a lake acidification starting in the 1920's. We propose that soil Fe was mobilized and runoff acidified by acidic precipitation and/or by soil acidification resulting from vegetational succession following reduced grazing. Even minor land use changes or disturbances in lake watersheds introduce ambiguity to the sedimentary evidence relating to atmospheric influences. Diatom counts from surface sediments in 36 N and 31 NE lakes were regressed against contemporary water pH to obtain coefficients for computing past pH from subsurface counts. Computed decreases of 0.3–0.8 pH units start between I890 and I930 in N lakes already acidic (pH 5.0–5.5) before the decrease. These and lesser decreases in other lakes start decades to over a century after the first sedimentary indications of atmospheric heavy metal pollution. It is proposed that the acidification of precipitation accompanied the metal pollution. The delays in lake acidification may be due to buffering by the lakes and watersheds. The magnitude of acidification and heavy metal loading of the lakes parallels air pollution gradients. Shift in cladoceran remains are contemporary with acidification, preceding elimination of fishes.
CUSTOMER WILLINGNESS TO PAY FOR ONLINE MUSIC : THE ROLE OF FREE MENTALITY

Free mentality, a strong belief that everything online should be free, has been ironed into people’s minds. This concept also significantly affects the development of e-commerce. However, despite the importance of this “free mentality” concept, a lack of systematic investigations inhibits our understanding of this issue. Based on the decomposed theory of planned behavior, this study focuses on free mentality and investigates its direct and interactive effects on the consumer’s attitude toward paying, which, in turn, affects the intention to pay for online music services. Data collected from 268 online music users illustrates the direct and negative impact of free mentality on attitude toward paying. The moderating role of free mentality on the relationship between perceived sacrifices and attitude toward paying is also illustrated. At the conclusion of this paper, we discuss the results and their implications for practitioners and academics.
Effects of Freestream Turbulence in a Model Wind Turbine Wake

The flow structure in the wake of a model wind turbine is explored under negligible and high turbulence in the freestream region of a wind tunnel at Re ∼ 7× 104. Attention is placed on the evolution of the integral scale and the contribution of the large-scale motions from the background flow. Hotwire anemometry was used to obtain the streamwise velocity at various streamwise and spanwise locations. The pre-multiplied spectral difference of the velocity fluctuations between the two cases shows a significant energy contribution from the background turbulence on scales larger than the rotor diameter. The integral scale along the rotor axis is found to grow linearly with distance, independent of the incoming turbulence levels. This scale appears to reach that of the incoming flow in the high turbulence case at x/d ∼ 35–40. The energy contribution from the turbine to the large-scale flow structures in the low turbulence case increases monotonically with distance. Its growth rate is reduced past x/d ∼ 6–7. There, motions larger than the rotor contribute ∼50% of the total energy, suggesting that the population of large-scale motions is more intense in the intermediate field. In contrast, the wake in the high incoming turbulence is quickly populated with large-scale motions and plateau at x/d ∼ 3.
A collaborative approach for improving the security of vehicular scenarios: The case of platooning

Autonomous vehicles platooning has received considerable attention in recent years, due to its potential to significantly benefit road transportation, improving traffic efficiency, enhancing road safety and reducing fuel consumption. The Vehicular ad hoc Networks and the de facto vehicular networking standard IEEE 802.11p communication protocol are key tools for the deployment of platooning applications, since the cooperation among vehicles is based on a reliable communication structure. However, vehicular networks can suffer different security threats. Indeed, in collaborative driving applications, the sudden appearance of a malicious attack can mainly compromise: (i) the correctness of data traffic flow on the vehicular network by sending malicious messages that alter the platoon formation and its coordinated motion; (ii) the safety of platooning application by altering vehicular network communication capability. In view of the fact that cyber attacks can lead to dangerous implications for the security of autonomous driving systems, it is fundamental to consider their effects on the behavior of the interconnected vehicles, and to try to limit them from the control design stage. To this aim, in this work we focus on some relevant types of malicious threats that affect the platoon safety, i.e. application layer attacks (Spoofing and Message Falsification) and network layer attacks (Denial of Service and Burst Transmission), and we propose a novel collaborative control strategy for enhancing the protection level of autonomous platoons. The control protocol is designed and validated in both analytically and experimental way, for the appraised malicious attack scenarios and for different communication topology structures. The effectiveness of the proposed strategy is shown by using PLEXE, a state of the art inter-vehicular communications and mobility simulator that includes basic building blocks for platooning. A detailed experimental analysis discloses the robustness of the proposed approach and its capabilities in reacting to the malicious attack effects.
A deep convolutional encoder-decoder model for robust speech dereverberation

Research shows that speech dereverberation (SD) with Deep Neural Network (DNN) achieves the state-of-the-art results by learning spectral mapping, which, simultaneously, lacks the characterization of the local temporal spectral structures (LTSS) of speech signal and calls for a large storage space that is impractical in real applications. Contrarily, the Convolutional Neural Network (CNN) offers a better modeling ability by considering local patterns and has less parameters with its weights sharing property, which motivates us to employ the CNN for SD task. In this paper, to our knowledge, a Deep Convolutional Encoder-Decoder (DCED) model is proposed for the first time in dealing with the SD task (DCED-SD), where the advantage of the DCED-SD model lies in its powerful LTSS modeling capability via convolutional encoder-decoder layers with smaller storage requirement. By taking the reverberant and anechoic spectrum as training pairs, the proposed DCED-SD is well-trained in a supervised manner with less convergence time. Additionally, the DCED-SD model size is 23 times smaller than the size of DNN-SD model with better performance achieved. By using the simulated and real-recorded data, extensive experiments have been conducted to demonstrate the superiority of DCED-based SD method over the DNN-based SD method under different unseen reverberant conditions.
POMDP-based control of workflows for crowdsourcing

a r t i c l e i n f o a b s t r a c t Crowdsourcing, outsourcing of tasks to a crowd of unknown people (" workers ") in an open call, is rapidly rising in popularity. It is already being heavily used by numerous employers (" requesters ") for solving a wide variety of tasks, such as audio transcription, content screening, and labeling training data for machine learning. However, quality control of such tasks continues to be a key challenge because of the high variability in worker quality. In this paper we show the value of decision-theoretic techniques for the problem of optimizing workflows used in crowdsourcing. In particular, we design AI agents that use Bayesian network learning and inference in combination with Partially-Observable Markov Decision Processes (POMDPs) for obtaining excellent cost-quality tradeoffs. We use these techniques for three distinct crowdsourcing scenarios: (1) control of voting to answer a binary-choice question, (2) control of an iterative improvement workflow, and (3) control of switching between alternate workflows for a task. In each scenario, we design a Bayes net model that relates worker competency, task difficulty and worker response quality. We also design a POMDP for each task, whose solution provides the dynamic control policy. We demonstrate the usefulness of our models and agents in live experiments on Amazon Mechanical Turk. We consistently achieve superior quality results than non-adaptive controllers, while incurring equal or less cost.
A survey of energy-efficient caching in information-centric networking

To better cope with the Internet usage shift from host-centric end-to-end communication to receiver-driven content retrieval, innovative information-centric networking (ICN) architectures have been proposed. A notable advantage of these novel networking architectures is to provide transparent and ubiquitous in-network caching to speed up content distribution and improve network resource utilization. With the explosive increase of global network traffic, the energy efficiency issue in ICN is a growing concern. In this article, we provide a brief survey of energy-efficient caching techniques in ICN from the placement, content placement, and requestto- cache routing perspectives. We also outline some challenges and future research directions about caching policies for green ICN.
A flexible approach for extracting metadata from bibliographic citations

In this article we present FLUX-CiM, a novel method for extracting components (e.g., author names, article titles, venues, page numbers) from bibliographic citations. Our method does not rely on patterns encoding specific delimiters used in a particular citation style.This feature yields a high degree of automation and flexibility, and allows FLUX-CiM to extract from citations in any given format. Differently from previous methods that are based on models learned from user-driven training, our method relies on a knowledge base automatically constructed from an existing set of sample metadata records from a given field (e.g., computer science, health sciences, social sciences, etc.). These records are usually available on the Web or other public data repositories. To demonstrate the effectiveness and applicability of our proposed method, we present a series of experiments in which we apply it to extract bibliographic data from citations in articles of different fields. Results of these experiments exhibit precision and recall levels above 94% for all fields, and perfect extraction for the large majority of citations tested. In addition, in a comparison against a stateof-the-art information-extraction method, ours produced superior results without the training phase required by that method. Finally, we present a strategy for using bibliographic data resulting from the extraction process with FLUX-CiM to automatically update and expand the knowledge base of a given domain. We show that this strategy can be used to achieve good extraction results even if only a very small initial sample of bibliographic records is available for building the knowledge base.
Management of Acute and Recurrent Gout: A Clinical Practice Guideline From the American College of Physicians.

The American College of Physicians (ACP) developed this guideline to present the evidence and provide clinical recommendations on the management of gout.



Using the ACP grading system, the committee based these recommendations on a systematic review of randomized, controlled trials; systematic reviews; and large observational studies published between January 2010 and March 2016. Clinical outcomes evaluated included pain, joint swelling and tenderness, activities of daily living, patient global assessment, recurrence, intermediate outcomes of serum urate levels, and harms.


Target Audience and Patient Population
The target audience for this guideline includes all clinicians, and the target patient population includes adults with acute or recurrent gout.


Recommendation 1
ACP recommends that clinicians choose corticosteroids, nonsteroidal anti-inflammatory drugs (NSAIDs), or colchicine to treat patients with acute gout. (Grade: strong recommendation, high-quality evidence).


Recommendation 2
ACP recommends that clinicians use low-dose colchicine when using colchicine to treat acute gout. (Grade: strong recommendation, moderate-quality evidence).


Recommendation 3
ACP recommends against initiating long-term urate-lowering therapy in most patients after a first gout attack or in patients with infrequent attacks. (Grade: strong recommendation, moderate-quality evidence).


Recommendation 4
ACP recommends that clinicians discuss benefits, harms, costs, and individual preferences with patients before initiating urate-lowering therapy, including concomitant prophylaxis, in patients with recurrent gout attacks. (Grade: strong recommendation, moderate-quality evidence).
Refine pedestrian detections by referring to features in different ways

The performance of object detection has been improved as the success of deep architectures. The main algorithm predominantly used for general detection is Faster R-CNN because of their high accuracy and fast inference time. In pedestrian detection, Region Proposal Network (RPN) itself which is used for region proposals in Faster R-CNN can be used as a pedestrian detector. Also, RPN even shows better performance than Faster R-CNN for pedestrian detection. However, RPN generates severe false positives such as high score backgrounds and double detections because it does not have downstream classifier. From this observations, we made a network to refine results generated from the RPN. Our Refinement Network refers to the feature maps of the RPN and trains the network to rescore severe false positives. Also, we found that different type of feature referencing method is crucial for improving performance. Our network showed better accuracy than RPN with almost same speed on Caltech Pedestrian Detection benchmark.
An AlN-based piezoelectric micro-machined ultrasonic transducer (pMUT) array

The development of wireless energy transfer is critical to the promotion of implantable medical devices (IMDs). In this paper, an aluminum nitride (AlN) based piezoelectric micromachined ultrasonic transducer (pMUT) array is designed, fabricated and characterized, which can be a potential power supply for IMDs. The pMUT array is ideally suitable for integration with wireless-powered system since it is fabricated from miniaturized AlN membrane array of 4×4 units. Both the simulation and experiment are conducted to characterize the pMUT device. The transmitting sensitivity of the fabricated pMUT array is measured as 83 nm/V at a resonant frequency of 527 kHz.
Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark

Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users’ devices, and increases the workload of market maintainers. In the space of six years, the research around this specific issue has produced 57 approaches which do not readily scale to millions of apps or are only evaluated on private datasets without, in general, tool support available to the community. Through a systematic literature review of the subject, we argue that the research is slowing down, where many state-of-the-art approaches have reported high-performance rates on closed datasets, which are unfortunately difficult to replicate and to compare against. In this work, we propose to reboot the research in repackaged app detection by providing a literature review that summarises the challenges and current solutions for detecting repackaged apps and by providing a large dataset that supports replications of existing solutions and implications of new research directions. We hope that these contributions will re-activate the direction of detecting repackaged apps and spark innovative approaches going beyond the current state-of-the-art.
Convolutional Sparse Autoencoders for Image Classification

Convolutional sparse coding (CSC) can model local connections between image content and reduce the code redundancy when compared with patch-based sparse coding. However, CSC needs a complicated optimization procedure to infer the codes (i.e., feature maps). In this brief, we proposed a convolutional sparse auto-encoder (CSAE), which leverages the structure of the convolutional AE and incorporates the max-pooling to heuristically sparsify the feature maps for feature learning. Together with competition over feature channels, this simple sparsifying strategy makes the stochastic gradient descent algorithm work efficiently for the CSAE training; thus, no complicated optimization procedure is involved. We employed the features learned in the CSAE to initialize convolutional neural networks for classification and achieved competitive results on benchmark data sets. In addition, by building connections between the CSAE and CSC, we proposed a strategy to construct local descriptors from the CSAE for classification. Experiments on Caltech-101 and Caltech-256 clearly demonstrated the effectiveness of the proposed method and verified the CSAE as a CSC model has the ability to explore connections between neighboring image content for classification tasks.
Polygonal approximation of digital planar curves through adaptive optimizations

In this paper, we present a novel non-parametric polygonal approximation algorithm for digital planar curves. The proposed algorithm first selects a set of points (called cut-points) on the contour which are of very ‘high’ curvature. An optimization procedure is then applied to find adaptively the best fitting polygonal approximations for the different segments of the contour as defined by the cut-points. The optimization procedure uses one of the efficiency measures for polygonal approximation algorithms as the objective function. Our algorithm adaptively locates segments of the contour with different levels of details. The proposed algorithm follows the contour more closely where the level of details on the curve is high, while addressing noise by using suppression techniques. This makes the algorithm very robust for noisy, real-life contours having different levels of details. The proposed algorithm performs favorably when compared with other polygonal approximation algorithms using the popular shapes. In addition, the effectiveness of the algorithm is shown by measuring its performance over a large set of handwritten Arabic characters and MPEG7 CE Shape-1 Part B database. Experimental results demonstrate that the proposed algorithm is very stable and robust compared with other algorithms.