nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle Routing Problems for Drone Delivery

Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. We propose two multitrip VRPs for drone delivery that address both issues. One minimizes costs subject to a delivery time limit, while the other minimizes the overall delivery time subject to a budget constraint. We mathematically derive and experimentally validate an energy consumption model for multirotor drones, demonstrating that energy consumption varies approximately linearly with payload and battery weight. We use this approximation to derive mixed integer linear programs for our VRPs. We propose a cost function that considers our energy consumption model and drone reuse, and apply it in a simulated annealing (SA) heuristic for finding suboptimal solutions to practical scenarios. To assist drone delivery practitioners with balancing cost and delivery time, the SA heuristic is used to show that the minimum cost has an inverse exponential relationship with the delivery time limit, and the minimum overall delivery time has an inverse exponential relationship with the budget. Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs.
Three-Dimensional Architecture of Substrate Integrated Waveguide Feeder for Fermi Tapered Slot Antenna Array Applications

A class of three-dimensional planar arrays in substrate integrated waveguide (SIW) technology is proposed, designed and demonstrated with 8 × 16 elements at 35 GHz for millimeter-wave imaging radar system applications. Endfire element is generally chosen to ensure initial high gain and broadband characteristics for the array. Fermi-TSA (tapered slot antenna) structure is used as element to reduce the beamwidth. Corrugation is introduced to reduce the resulting antenna physical width without degradation of performance. The achieved measured gain in our demonstration is about 18.4 dBi. A taper shaped air gap in the center is created to reduce the coupling between two adjacent elements. An SIW H-to-E-plane vertical interconnect is proposed in this three-dimensional architecture and optimized to connect eight 1 × 16 planar array sheets to the 1 × 8 final network. The overall architecture is exclusively fabricated by the conventional PCB process. Thus, the developed SIW feeder leads to a significant reduction in both weight and cost, compared to the metallic waveguide-based counterpart. A complete antenna structure is designed and fabricated. The planar array ensures a gain of 27 dBi with low SLL of 26 dB and beamwidth as narrow as 5.15 degrees in the E-plane and 6.20 degrees in the 45°-plane.
Instance-Level Segmentation with Deep Densely Connected MRFs

Our aim is to provide a pixel-level object instance labeling of a monocular image. We build on recent work  that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in  to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field and show how to encode various intuitive potentials in a way that is amenable to efficient mean field inference [13]. Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [8] demonstrate that our method achieves a significant performance boost over the baseline [27].
Unifying Zero-Knowledge Proofs of Knowledge

We present a simple zero-knowledge proof of knowledge protocol of which many protocols in the literature are instantiations. These include Schnorr’s protocol for proving knowledge of a discrete logarithm, the Fiat-Shamir and Guillou-Quisquater protocols for proving knowledge of a modular root, protocols for proving knowledge of representations (like Okamoto’s protocol), protocols for proving equality of secret values, a protocol for proving the correctness of a Diffie-Hellman key, protocols for proving the multiplicative relation of three commitments (as required in secure multi-party computation), and protocols used in credential systems. This shows that a single simple treatment (and proof), at a high level of abstraction, can replace the individual previous treatments. Moreover, one can devise new instantiations of the protocol.
Voronoi treemaps

Treemaps are a well known method for the visualization of attributed hierarchical data. Previously proposed treemap layout algorithms are limited to rectangular shapes, which cause problems with the aspect ratio of the rectangles as well as with identifying the visualized hierarchical structure. The approach of Voronoi treemaps presented in this paper eliminates these problems through enabling subdivisions of and in polygons. Additionally, this allows for creating treemap visualizations within areas of arbitrary shape, such as triangles and circles, thereby enabling a more flexible adaptation of treemaps for a wider range of applications.
Thunderella: Blockchains with Optimistic Instant Confirmation

State machine replication, or “consensus”, is a central abstraction for distributed systems where a set of nodes seek to agree on an ever-growing, linearly-ordered log. In this paper, we propose a practical new paradigm called Thunderella for achieving state machine replication by combining a fast, asynchronous path with a (slow) synchronous “fall-back” path (which only gets executed if something goes wrong); as a consequence, we get simple state machine replications that essentially are as robust as the best synchronous protocols, yet “optimistically” (if a super majority of the players are honest), the protocol “instantly” confirms transactions. We provide instantiations of this paradigm in both permissionless (using proof-of-work) and permissioned settings. Most notably, this yields a new blockchain protocol (for the permissionless setting) that remains resilient assuming only that a majority of the computing power is controlled by honest players, yet optimistically—if 3/4 of the computing power is controlled by honest players, and a special player called the “accelerator”, is honest—transactions are confirmed as fast as the actual message delay in the network. We additionally show the 3/4 optimistic bound is tight for protocols that are resilient assuming only an honest majority.
Constrained Centroidal Voronoi Tessellations for Surfaces

Centroidal Voronoi tessellations are useful for subdividing a region in Euclidean space into Voronoi regions whose generators are also the centers of mass, with respect to a prescribed density function, of the regions. Their extensions to general spaces and sets are also available; for example, tessellations of surfaces in a Euclidean space may be considered. In this paper, a precise definition of such constrained centroidal Voronoi tessellations (CCVTs) is given and a number of their properties are derived, including their characterization as minimizers of an “energy.” Deterministic and probabilistic algorithms for the construction of CCVTs are presented and some analytical results for one of the algorithms are given. Computational examples are provided which serve to illustrate the high quality of CCVT point sets. Finally, CCVT point sets are applied to polynomial interpolation and numerical integration on the sphere.
Wireless Sensor and Networking Technologies for Swarms of Aquatic Surface Drones

In this paper, we discuss wireless sensor and networking technologies for swarms of inexpensive aquatic surface drones in the context of the HANCAD project. The goal is to enable the swarm to perform maritime tasks such as sea-border patrolling and environmental monitoring, while keeping the cost of each drone low. Communication between drones is essential for the success of the project. Preliminary experiments show that XBee modules are promising for energy efficient multi-hop drone-to-drone communication.
A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms

In this paper we introduce a multilingual Named Entity Recognition (NER) system that uses statistical modeling techniques. The system identifies and classifies NEs in the Hungarian and English languages by applying AdaBoostM1 and the C4.5 decision tree learning algorithm. We focused on building as large a feature set as possible, and used a split and recombine technique to fully exploit its potentials. This methodology provided an opportunity to train several independent decision tree classifiers based on different subsets of features and combine their decisions in a majority voting scheme. The corpus made for the CoNLL 2003 conference and a segment of Szeged Corpus was used for training and validation purposes. Both of them consist entirely of newswire articles. Our system remains portable across languages without requiring any major modification and slightly outperforms the best system of CoNLL 2003, and achieved a 94.77% F measure for Hungarian. The real value of our approach lies in its different basis compared to other top performing models for English, which makes our system extremely successful when used in combination with CoNLL modells.
Variable On-Time (VOT)-Controlled Critical Conduction Mode Buck PFC Converter for High-Input AC/DC HB-LED Lighting Applications

For high input voltage (>;264 Vac) ac/dc applications, a buck power factor correction (PFC) converter is a good choice because of its low output voltage, high efficiency, lifetime improvement, and cost reduction by using a low voltage rating (<;200 V) electrolytic capacitor. However, due to the inherent dead angle of the input current, the harmonics of the buck PFC converter are high, which limits its application in lighting systems. In order to make the buck PFC converter meet the harmonics requirements (IEC61000-3-2, Class C) in lighting applications, this paper proposes a variable on-time controller for a critical conduction mode (CRM) buck PFC front-end converter for isolated high-brightness LED applications. By feedforwarding the input voltage and regulating the on-time of the switch, the high-order harmonics can be reduced to meet the lighting system limitations. Experimental results obtained on a 150-W CRM buck front-end PFC prototype show that the efficiency of buck PFC exceeds 96% during the entire line input range (250-530 Vac) at full load, and the current harmonics content can meet the harmonic requirements.
Bilingual Word Embeddings from Non-Parallel Document-Aligned Data Applied to Bilingual Lexicon Induction

We propose a simple yet effective approach to learning bilingual word embeddings (BWEs) from non-parallel document-aligned data (based on the omnipresent skip-gram model), and its application to bilingual lexicon induction (BLI). We demonstrate the utility of the induced BWEs in the BLI task by reporting on benchmarking BLI datasets for three language pairs: (1) We show that our BWE-based BLI models significantly outperform the MuPTM-based and context-counting models in this setting, and obtain the best reported BLI results for all three tested language pairs; (2) We also show that our BWE-based BLI models outperform other BLI models based on recently proposed BWEs that require parallel data for bilingual training.
Learning to overtake in TORCS using simple reinforcement learning

In modern racing games programming non-player characters with believable and sophisticated behaviors is getting increasingly challenging. Recently, several works in the literature suggested that computational intelligence can provide effective solutions to support the development process of NPCs. In this paper, we applied the Behavior Analysis and Training (BAT) methodology to define a behavior-based architecture for the NPCs in The Open Racing Car Simulator (TORCS), a well-known open source racing game. Then, we focused on two major overtaking behaviors: (i) the overtaking of a fast opponent either on a straight stretch or on a large bend; (ii) the overtaking on a tight bend, which typically requires a rather advanced braking policy. We applied simple reinforcement learning, namely Q-learning, to learn both these overtaking behaviors. We tested our approach in several overtaking situations and compared the learned behaviors against one of the best NPC provided with TORCS. Our results suggest that, exploiting the proposed behavior-based architecture, Q-learning can effectively learn sophisticated behaviors and outperform programmed NPCs. In addition, we also show that the same approach can be successfully applied to adapt a previously learned behavior to a dynamically changing game situation.
Computing similarity of coarse and irregular trajectories using space-time prisms

Increasing volumes of trajectory data require analysis methods which go beyond the visual. Methods for computing trajectory analysis typically assume linear interpolation between quasi-regular sampling points. This assumption, however, is often not realistic, and can lead to a meaningless analysis for sparsely and/or irregularly sampled data. We propose to use the space-time prism model instead, allowing to represent the influence of speed on possible trajectories within a volume. We give definitions for the similarity of trajectories in this model and describe algorithms for its computation using the Fréchet and the equal time distance.
A systematic literature review of stakeholder identification methods in requirements elicitation

This paper presents a systematic review of relevant published studies related to topics in Requirements Engineering, specifically, concerning stakeholder identification methods in requirements elicitation, dated from 1984-2011. Addressing four specific research questions, this systematic literature review shows the following evidence gathered from these studies: current status of stakeholder identification in software requirement elicitation, the best practices recommended for its performance, consequences of incorrect identification in requirements quality, and, aspects which need to be improved. Our findings suggest that the analyzed approaches still have serious limitations in terms of covering all aspects of stakeholder identification as an important part of requirements elicitation. However, through correctly identifying and understanding the stakeholders, it is possible to develop high quality software.
Demonstration of 3 kV 4H-SiC reverse blocking MOSFET

The authors developed 3 kV 4H-SÍC reverse blocking (RB) metal-oxide-semiconductor field-effect transistors (MOSFETs) for the first time. To achieve reverse blocking capability, the n+-substrate layer was removed by polishing, and both a Schottky contact and edge-termination structure were introduced onto the wafer backside. Fabricated SiC RB MOSFETs exhibited good Schottky characteristics, and measured differential specific on-resistance was 20 mΩ·cm2. Both forward and reverse blocking voltages of RB MOSFETs are higher than 3 kV. On-state power loss of a developed RB MOSFET is 35% lower than that of anti-serially connected standard 3 kV SiC MOSFETs, demonstrating the advantage of the developed RB MOSFET as a high-voltage bi-directional switch.
Translating from Complex to Simplified Sentences

We address the problem of simplifying Portuguese texts at the sentence level by treating it as a "translation task". We use the Statistical Machine Translation (SMT) framework to learn how to translate from complex to simplified sentences. Given a parallel corpus of original and simplified texts, aligned at the sentence level, we train a standard SMT system and evaluate the "translations" produced using both standard SMT metrics like BLEU and manual inspection. Results are promising according to both evaluations, showing that while the model is usually overcautious in producing simplifications, the overall quality of the sentences is not degraded and certain types of simplification operations, mainly lexical, are appropriately captured.
Understanding the Mechanisms of Deep Transfer Learning for Medical Images

The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20% higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.
Microarchitecture optimizations for exploiting memory-level parallelism

The performance of memory-bound commercial applicationssuch as databases is limited by increasing memory latencies. Inthis paper, we show that exploiting memory-level parallelism(MLP) is an effective approach for improving the performance ofthese applications and that microarchitecture has a profound impacton achievable MLP. Using the epoch model of MLP, we reasonhow traditional microarchitecture features such as out-of-orderissue and state-of-the-art microarchitecture techniques suchas runahead execution affect MLP. Simulation results show that amoderately aggressive out-of-order issue processor improvesMLP over an in-order issue processor by 12-30%, and that aggressivehandling of loads, branches and serializing instructionsis needed to attain the full benefits of large out-of-order instructionwindows. The results also show that a processor's issue windowand reorder buffer should be decoupled to exploit MLP more efficiently.In addition, we demonstrate that runahead execution ishighly effective in enhancing MLP, potentially improving the MLPof the database workload by 82% and its overall performance by60%. Finally, our limit study shows that there is considerableheadroom in improving MLP and overall performance by implementingeffective instruction prefetching, more accurate branchprediction and better value prediction in addition to runahead execution.
On the uniqueness of Web browsing history patterns

We present the results of the first large-scale study of the uniqueness of Web browsing histories, gathered from a total of 368, 284 Internet users who visited a history detection demonstration website. Our results show that for a majority of users (69%), the browsing history is unique and that users for whom we could detect at least 4 visited websites were uniquely identified by their histories in 97% of cases. We observe a significant rate of stability in browser history fingerprints: for repeat visitors, 38% of fingerprints are identical over time, and differing ones were correlated with original history contents, indicating static browsing preferences (for history subvectors of size 50). We report a striking result that it is enough to test for a small number of pages in order to both enumerate users’ interests and perform an efficient and unique behavioral fingerprint; we show that testing 50 web pages is enough to fingerprint 42% of users in our database, increasing to 70% with 500 web pages. Finally, we show that indirect history data, such as information about categories of visited websites can also be effective in fingerprinting users, and that similar fingerprinting can be performed by common script providers such as Google or Facebook.
Next generation IMA configuration engineering - from architecture to application

Integrated Modular Avionics (IMA) as successor of conventional avionics have many advantages with respect to flexibility, re-usabilty and standardisation. Systems based on IMA consist of a generic hardware platform with a standardised hardware- and software interface and dedicated functions for module management. A configuration defines how system applications are integrated to the hardware modules and is an important aspect of the IMA development process. Despite the development process for conventional avionics and due to the fact that many, probably independent systems share the same platform the development process is heavily distributed. Therefore, the integration effort causes new challenges. To reduce the complexity of the distributed configuration process, a flexible and extensible configuration engineering software framework has been developed and is presented that contributes to an enhanced configuration process. Besides the standard tasks to manage, create, modify and verify configuration data it also allows a graphical representation while dedicated im- and export functions enable to re-use existing configuration data and to integrate the new software framework transparently into an existing process. One of the central functions are early consistency checks so that at the time of integration the configuration is error-free at best. This article focusses mainly on the software architectural aspects.
A prediction and planning framework for road safety analysis , obstacle avoidance and driver information

This paper presents a prediction and planning framework for analysing the safety and interaction of moving objects in complex road scenes. Rather than detecting specific, known, dangerous configurations, we simulate all the possible motion and interaction of objects. This simulation is used to detect dangerous situations, and to select the best path. The best path can be chosen according to a number of different criterion, such as: smoothest motion, largest avoiding distance, or quickest path. This framework can be applied, either as a driver warning system (open loop), or as an action recommendation system (human in the loop), or as an intelligent cruise control system (closed loop). This framework is evaluated using synthetic data, using simple and complex road scenes.
Digital Maturity in Traditional industries - an Exploratory Analysis

The diffusion of new digital technologies renders digital transformation relevant for nearly every industry. Therefore, the maturity of firms in mastering this fundamental organizational change is increasingly discussed in practice-oriented literature. These studies, however, suffer from some shortcomings. Most importantly, digital maturity is typically described along a linear scale, thus assuming that all firms do and need to proceed through the same path. We challenge this assumption and derive a more differentiated classification scheme based on a comprehensive literature review as well as an exploratory analysis of a survey on digital transformation amongst 327 managers. Based on these findings we propose two scales for describing a firm’s digital maturity: first, the impact that digital transformation has on a specific firm; second, the readiness of the firm to master the upcoming changes. We demonstrate the usefulness of this two scale measure by empirically deriving five digital maturity clusters as well as further empirical evidence. Our framework illuminates the monolithic block of digital maturity by allowing for a more differentiated firm-specific assessment – thus, it may serve as a first foundation for future research on digital maturity.