nils-herrmann/clean_scirepeval huggingface dataset:
Doubly Fed Induction Generator Systems For Variable Speed Wind Turbine

This paper presents results of a study concerning the dynamic behaviour of a wind energy system powered by a doubly fed induction generator with the rotor connected to the electric network through an AC-AC converter. A tendency to put up more and more wind turbines can be observed all over the world. Also, there is awareness in taking into account the requirements of a clean environment, due to the need to preserve our habitat. Renewable energy sources not contributing to the enhanced greenhouse effect, especially wind power, are becoming an important component of the total generation. Hence, research concerning the dynamic behaviour of wind energy systems is important to achieve a better knowledge.
Hausdorff and Minimal Distances between Parametric Freeforms in R2 and R3

We present algorithms to derive the precise Hausdorff distance and/or the minimal distance between two freeform shapes, either curves or surfaces, in IR or IR. The events at which the Hausdorff/minimal distance can occur are identified and means to efficiently compute these events are presented. Examples are also shown and the extension to arbitrary dimensions is briefly discussed.
MCMAS: A Model Checker for the Verification of Multi-Agent Systems

While temporal logic in its various forms has proven essenti al to reason about reactive systems, agent-based scenarios are typically specified by c onsidering high-level agents attitudes. In particular, specification languages based on epistemic logic [7], or logics for knowledge, have proven useful in a variety of areas inclu ding robotics, security protocols, web-services, etc. For example, security speci fications involving anonymity  are known to be naturally expressible in epistemic forma lisms as they explicitly state the lack of different kinds of knowledge of the principals. More generally, various extensions of temporal logic have b e n studied in agents and AI contexts to represent properties of autonomous syste ms. In addition to epistemic operators, at the very core of these approaches is the import ance of deontic modalities expressing norms and compliance/violation with respect to previously agreed commitments, and ATL-like modalities expressing cooperation amo ng agents. While these languages have been long explored and appropria te semantics developed, until recently there has been a remarkable gap in the av ailability of efficient symbolic model checking toolkits supporting these. In this pap er we describe MCMAS, a symbolic model checker specifically tailored to agent-bas ed specifications and scenarios.  supports specifications based on CTL, epi st mic logic (including operators of common and distributed knowledge) , Alt ernating Time Logic , and deontic modalities for correctness [16]. The release de cribed in this abstract is a complete rebuild of a preliminary experimental checker [1 4]. The model input language includes variables and basic types and it implements t he semantics of interpreted systems, thereby naturally supporting the modularity pres ent in agent-based systems. MCMAS implements OBDD-based algorithms optimised for inte rpr ted systems and supports fairness, counter-example generation, and inter active execution (both in explicit and symbolic mode). MCMAS has been used in a variety of scenarios including web-services, diagnosis, and security. MCMAS is released u nder GNU-GPL.
Learning Depth from Single Monocular Images

We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured outdoor environments which include forests, trees, buildings, etc.) and their corresponding ground-truth depthmaps. Then, we apply supervised learning to predict the depthmap as a function of the image. Depth estimation is a challenging problem, since local features alone are insufficient to estimate depth at a point, and one needs to consider the global context of the image. Our model uses a discriminatively-trained Markov Random Field (MRF) that incorporates multiscale localand global-image features, and models both depths at individual points as well as the relation between depths at different points. We show that, even on unstructured scenes, our algorithm is frequently able to recover fairly accurate depthmaps.
Design of DC/DC Boost converter with FNN solar cell Maximum Power Point Tracking controller

This paper demonstrates the Maximum Power Point Tracking (MPPT) controller that uses a DC/DC Boost converter with a Fuzzy Neural Network (FNN) system. It simplifies the topology of the DC/DC boost converter model to state equations, which is easy to simulate with Matlab. Additionally, the FNN system uses an integrated Fuzzy and Neural Network (NN), which advantages include uncertainty information processing and neural network learning. After assigning a suitable structure, we adjust the membership function and assign the algorithm weighting to track the maximum power point effectively in the parameters leaning process. The simulation result has verified the system to be efficient and rapid in tracking the MPP and converting the power from solar cells into the battery bank.
Using the Kinect for detecting tremors: Challenges and opportunities

The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.
Visibility estimation and joint inpainting of lidar depth maps

This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.
Distributed Spatial Keyword Querying on Road Networks

Spatial-keyword queries on road networks are receiving increasing attention with the prominence of location-based services. There is a growing need to handle queries on road networks in distributed environments because a large network is typically distributed over multiple machines and it will improve query throughput. However, all the existing work on spatial keyword queries is based on a centralized setting. In this paper, we develop a distributed solution to answering spatial keyword queries on road networks. Example queries include “find locations near a supermarket and a hospital,” and “find Chinese restaurants within 500 meters from my current location.” We define an operation for answering such queries and reduce the problem of answering a query into computing a function of such operations. We propose a new distributed index that enables each machine to independently evaluate the operation on its network fragment in a distributed setting. We theoretically prove the space optimality of the proposed index technique. We conduct experiments with a distributed setting. Experimental results demonstrate the promising performance of our method.
MICS: an efficient content space representation model for publish/subscribe systems

One of the main challenges faced by content-based publish/subscribe systems is handling large amount of dynamic subscriptions and publications in a multidimensional content space. To reduce subscription forwarding load and speed up content matching, subscription covering, subsumption and merging techniques have been proposed. In this paper we propose MICS, Multidimensional Indexing for Content Space that provides an efficient representation and processing model for large number of subscriptions and publications. MICS creates a one dimensional representation for publications and subscriptions using Hilbert space filling curve. Based on this representation, we propose novel content matching and subscription management (covering, subsumption and merging) algorithms. Our experimental evaluation indicates that the proposed approach significantly speeds up subscription management operations compared to the naive linear approach.
Linking GloVe with word2vec

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al.  is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS)  implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.
Understanding Cyber Attack Behaviors with Sentiment Information on Social Media

In today’s increasingly connected world, cyber attacks have become a serious threat with detrimental effects on individuals, businesses, and broader society. Truly mitigating the negative impacts of these attacks requires a deeper understanding of malicious cyber activities and the capability of predicting these attacks before they occur. However, detecting the occurrence of cyber attacks is non-trivial due to the anonymity of cyber attacks and the ambiguity or unavailability of network data collected within organizations. Thus, we need to explore more nuanced auxiliary information that can provide improved predictive power and insight into the behavioral factors involved in planning and executing a cyber attack. Evidence suggests that public discourse in online sources, such as social media, is strongly correlated with the occurrence of real-world behavior; we believe this same premise can provide predictive indicators of cyber attacks. For example, extreme negative sentiments towards an organization may indicate a higher probability that it will be the target of a cyber attack. In this paper, we propose to use sentiment in social media as a sensor to better understand, detect, and predict cyber attacks. We develop an effective unsupervised sentiment predictor model utilizing emotional signals, such as emoticons or punctuation, common in social media communications, and a method for using this model as part of a logistic regression predictor to correlate changes in sentiment to the probability of an attack. Experiments on real-world social media data around well-known hacktivist attacks demonstrate the efficacy of the proposed sentiment model for cyber attack understanding and prediction.
Segmentation of color image using adaptive thresholding and masking with watershed algorithm

The individualization of an object from a digital image is a common problem in the field of image processing. We propose a modified version of the watershed algorithm for image segmentation. We have presented an adaptive masking and a threshoding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of the 10 different kinds of color images. The experimental results are obtained using image quality assessment (IQA) metrics such as PSNR, MSE, PSNRRGB and Color Image Quality Measure (CQM) based on reversible YUV color transformation. Consequently, the proposed modified watershed approach can enhance the image segmentation performance. Similarly, it is worth noticing that our proposed MWS approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than three other algorithms.
Real Time Video Data Mining for Surveillance Video Streams

We extend our previous work  of the general framework for video data mining to further address the issue such as how to mine video data. To extract motions, we use an accumulation of quantized pixel differences among all frames in a video segment. As a result, the accumulated motions of segment are represented as a two dimensional matrix. Further, we develop how to capture the location of motions occurring in a segment using the same matrix generated for the calculation of the amount. We study how to cluster those segmented pieces using the features (the amount and the location of motions) we extract by the matrix above. We investigate an algorithm to find whether a segment has normal or abnormal events by clustering and modeling normal events, which occur mostly. In addition to deciding normal or abnormal, the algorithm computes Degree of Abnormality of a segment, which represents to what extent a segment is distant to the existing segments in relation with normal events. Our experimental studies indicate that the proposed techniques are promising.
Fast and Accurate Closest Point Search on Triangulated Surfaces and its Application to Head Motion Estimation

The Iterative Closest Point (ICP) algorithm is widely used to register two roughly aligned surfaces. Its most expensive step is the search for the closest point. Many efficient variants have been proposed to speed up this step, however they do not guarantee that the closest point on a triangulated surface is found. Instead they might result in an approximation of the closest point. In this paper we present a method for the closest point search that is fast and exact. The method was implemented and used to evaluate the accuracy of head motion estimation using dense 3D data obtained from a stereo camera system. The results show that the accuracy of the estimated head motion is better than 1 mm for translational movements and better than 1 degree for rotations.  Iterative Closest Point, 3D Registration, Stereo, Head Motion
Work Item Tagging: Communicating Concerns in Collaborative Software Development

In collaborative software development projects, work items are used as a mechanism to coordinate tasks and track shared development work. In this paper, we explore how “tagging,” a lightweight social computing mechanism, is used to communicate matters of concern in the management of development tasks. We present the results from two empirical studies over 36 and 12 months, respectively, on how tagging has been adopted and what role it plays in the development processes of several professional development projects with more than 1,000 developers in total. Our research shows that the tagging mechanism was eagerly adopted by the teams, and that it has become a significant part of many informal processes. Different kinds of tags are used by various stakeholders to categorize and organize work items. The tags are used to support finding of tasks, articulation work, and information exchange. Implicit and explicit mechanisms have evolved to manage the tag vocabulary. Our findings indicate that lightweight informal tool support, prevalent in the social computing domain, may play an important role in improving team-based software development practices.
A survey on unmanned aerial vehicle collision avoidance systems

Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.
Scheduling Real-Time Transactions: A Performance Evaluation

Managing transactions with real-time requirements presents many new problems. In this paper we address several: How can we schedule transactions with deadlines? How do the real-time constraints affect concurrency control? How should overloads be handled? How does the scheduling of 1/0 requests affect the timeliness of transactions? How should exclusive and shared locking be handled? We describe a new group of algorithms for scheduling real-time transactions that produce serializable schedules. We present a model for scheduling transactions with deadlines on a single processor disk resident database system, and evaluate the scheduling algorithms through detailed simulation experiments.
Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges

In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, a large number of amputees still rejects prostheses or points out a low satisfaction level, due to a sub-optimal interaction between the socket and the residual limb tissues. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) affecting the stump-socket interface and reducing the comfort/stability of limb prostheses. In this review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. The paper then describes the technological solutions available to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.
Design, modeling and control of an omni-directional aerial vehicle

In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design possesses full force and torque authority in all three dimensions. A control strategy that allows for exploiting the vehicle's decoupled translational and rotational dynamics is introduced. A prototype of the proposed vehicle design is built using reversible motor-propeller actuators and capable of flying at any orientation. Preliminary experimental results demonstrate the feasibility of the novel design and the capabilities of the vehicle.
An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.