nils-herrmann/clean_scirepeval huggingface dataset:
A classification of location privacy attacks and approaches

In recent years, location-based services have become very popular, mainly driven by the availability of modern mobile devices with integrated position sensors. Prominent examples are points of interest finders or geo-social networks such as Facebook Places, Qype, and Loopt. However, providing such services with private user positions may raise serious privacy concerns if these positions are not protected adequately. Therefore, location privacy concepts become mandatory to ensure the user’s acceptance of location-based services. Many different concepts and approaches for the protection of location privacy have been described in the literature. These approaches differ with respect to the protected information and their effectiveness against different attacks. The goal of this paper is to assess the applicability and effectiveness of location privacy approaches systematically. We first identify different protection goals, namely personal information (user identity), spatial information (user position), and temporal information (identity/position + time). Secondly, we give an overview of basic principles and existing approaches to protect these privacy goals. In a third step, we classify possible attacks. Finally, we analyze existing approaches with respect to their protection goals and their ability to resist the introduced attacks.
Air-filled SIW transmission line and phase shifter for high-performance and low-cost U-Band integrated circuits and systems

In this paper, air-filled Substrate Integrated Waveguide (SIW) is proposed and demonstrated for the first time at U-band. This low-loss transmission line is developed on a low-cost multilayer Printed Circuit Board (PCB) process. The top and bottom layers may make use of an extremely low-cost standard substrate such as FR-4 on which base-band or digital circuits can be designed so to obtain a very compact, high performance, low-cost and self-packaged integrated system. For measurement purposes, an optimized-length dielectric- to air-filled SIW transition operating at U-band with 0.21 ±0.055 dB insertion loss is developed. The measured insertion loss of an air-filled SIW of interest at U-band is 0.122 ±0.122 dB/cm compared to 0.4 ±0.13 dB/cm for its dielectric-filled counterpart. Furthermore, an air-filled SIW phase shifter is reported for the first time. It achieves a measured 0.15 ±0.14 dB transmission loss at U-band. The proposed air-filled SIW transmission line and phase shifter are of particular interest for high performance and low-cost millimeter-wave circuits and systems.
An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010

This paper summarizes results of the 1st Contest on Semantic Description of Human Activities (SDHA), in conjunction with ICPR 2010. SDHA 2010 consists of three types of challenges, High-level Human Interaction Recognition Challenge, Aerial View Activity Classification Challenge, and Wide-Area Activity Search and Recognition Challenge. The challenges are designed to encourage participants to test existing methodologies and develop new approaches for complex human activity recognition scenarios in realistic environments. We introduce three new public datasets through these challenges, and discuss results of state-ofthe-art activity recognition systems designed and implemented by the contestants. A methodology using a spatio-temporal voting [19] successfully classified segmented videos in the UT-Interaction datasets, but had a difficulty correctly localizing activities from continuous videos. Both the method using local features [10] and the HMM based method  recognized actions from low-resolution videos (i.e. UT-Tower dataset) successfully. We compare their results in this paper.
From Smart Cities to Human Smart Cities

This paper argues that current technology-driven implementations of Smart Cities, although being an important step in the right direction, fall short in exploiting the most important human dimension of cities. The paper argues therefore in support of the concept of Human Smart Cities. In a Human Smart City, people rather than technology are the true actors of the urban "smartness". The creation of a participatory innovation ecosystem in which citizens and communities interact with public authorities and knowledge developers is key. Such collaborative interaction leads to co-designed user centered innovation services and calls for new governance models. The urban transformation in which citizens are the main "drivers of change" through their empowerment and motivation ensures that the major city challenges can be addressed, including sustainable behavior transformations. Furthermore, the authors argue that the city challenges can be more effectively addressed at the scale of neighborhood and they provide examples and experiences that demonstrate the viability, importance and impact of such approach. The paper builds on the experience of implementing Human Smart Cities projects in 27 European cities located in 17 different countries. Details of the technologies, methodologies, tools and policies are illustrated with examples extracted from the project My Neighbourhood.
Optical properties of human skin , subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm

The optical properties of human skin, subcutaneous adipose tissue and human mucosa were measured in the wavelength range 400–2000 nm. The measurements were carried out using a commercially available spectrophotometer with an integrating sphere. The inverse adding–doubling method was used to determine the absorption and reduced scattering coefficients from the measurements.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficiently generate object shapes in the form of dense point clouds. We use 2D convolutional operations to predict the 3D structure from multiple viewpoints and jointly apply geometric reasoning with 2D projection optimization. We introduce the pseudo-renderer, a differentiable module to approximate the true rendering operation, to synthesize novel depth maps for optimization. Experimental results for single-image 3D object reconstruction tasks show that we outperforms state-of-the-art methods in terms of shape similarity and prediction density.
Asynchronous verifiable secret sharing and proactive cryptosystems

Verifiable secret sharing is an important primitive in distributed cryptography. With the growing interest in the deployment of threshold cryptosystems in practice, the traditional assumption of a synchronous network has to be reconsidered and generalized to an asynchronous model. This paper proposes the first practical verifiable secret sharing protocol for asynchronous networks. The protocol creates a discrete logarithm-based sharing and uses only a quadratic number of messages in the number of participating servers. It yields the first asynchronous Byzantine agreement protocol in the standard model whose efficiency makes it suitable for use in practice. Proactive cryptosystems are another important application of verifiable secret sharing. The second part of this paper introduces proactive cryptosystems in asynchronous networks and presents an efficient protocol for refreshing the shares of a secret key for discrete logarithm-based sharings.
Map-supervised road detection

We propose an approach to detect drivable road area in monocular images. It is a self-supervised approach which doesn't require any human road annotations on images to train the road detection algorithm. Our approach reduces human labeling effort and makes training scalable. We combine the best of both supervised and unsupervised methods in our approach. First, we automatically generate training road annotations for images using OpenStreetMap1, vehicle pose estimation sensors, and camera parameters. Next, we train a Convolutional Neural Network (CNN) for road detection using these annotations. We show that we are able to generate reasonably accurate training annotations in KITTI data-set . We achieve state-of-the-art performance among the methods which do not require human annotation effort.
Gamma-Pareto Distribution and Its Applications

A new distribution, the gamma-Pareto, is defined and studied and various properties of the distribution are obtained. Results for moments, limiting behavior and entropies are provided. The method of maximum likelihood is proposed for estimating the parameters and the distribution is applied to fit three real data sets.  The Pareto distribution was named after Swiss economist Vilfredo Pareto (1848-1923) who discovered it while studying distributions for modeling income in Switzerland. Since that time, the Pareto distribution has been widely used in modeling heavy-tailed distributions, such as income distribution. Many applications of the Pareto distribution in economics, biology and physics can be found throughout the literature. Burroughs and Tebbens (2001) discussed applications of the Pareto distribution in modeling earthquakes, forest fire areas and oil and gas field sizes, and Schroeder, et al. (2010) presented an application of the Pareto distribution in modeling disk drive sector errors. To add flexibility to the Pareto distribution, various generalizations of the distribution have been derived, including: the generalized Pareto distribution (Pickands, 1975), the beta-Pareto distribution (Akinsete, et al., 2008), and the beta generalized Pareto distribution (Mahmoudi, 2011).
Sampling from multimodal distributions using tempered transitions

I present a new Markov chain sampling method appropriate for distributions with isolated modes. Like the recently-developed method of \simulated tempering", the \tempered transition" method uses a series of distributions that interpolate between the distribution of interest and a distribution for which sampling is easier. The new method has the advantage that it does not require approximate values for the normalizing constants of these distributions, which are needed for simulated tempering, and can be tedious to estimate. Simulated tempering performs a random walk along the series of distributions used. In contrast, the tempered transitions of the new method move systematically from the desired distribution, to the easily-sampled distribution, and back to the desired distribution. This systematic movement avoids the ineeciency of a random walk, an advantage that unfortunately is cancelled by an increase in the number of interpolating distributions required. Because of this, the sampling eeciency of the tempered transition method in simple problems is similar to that of simulated tempering. On more complex distributions, however, simulated tempering and tempered transitions may perform differently. Which is better depends on the ways in which the interpolating distributions are \deceptive".
Structure and appearance optimization for controllable shape design

The field of topology optimization seeks to optimize shapes under structural objectives, such as achieving the most rigid shape using a given quantity of material. Besides optimal shape design, these methods are increasingly popular as design tools, since they automatically produce structures having desirable physical properties, a task hard to perform by hand even for skilled designers. However, there is no simple way to control the appearance of the generated objects.
 In this paper, we propose to optimize shapes for both their structural properties and their appearance, the latter being controlled by a user-provided pattern example. These two objectives are challenging to combine, as optimal structural properties fully define the shape, leaving no degrees of freedom for appearance. We propose a new formulation where appearance is optimized as an objective while structural properties serve as constraints. This produces shapes with sufficient rigidity while allowing enough freedom for the appearance of the final structure to resemble the input exemplar.
 Our approach generates rigid shapes using a specified quantity of material while observing optional constraints such as voids, fills, attachment points, and external forces. The appearance is defined by examples, making our technique accessible to casual users. We demonstrate its use in the context of fabrication using a laser cutter to manufacture real objects from optimized shapes.
Effects of running, static stretching and practice jumps on explosive force production and jumping performance.

The interaction between running, stretching and practice jumps during warm-up for jumping tests has not been investigated. The purpose of the present study was to compare the effects of running, static stretching of the leg extensors and practice jumps on explosive force production and jumping performance.



Sixteen volunteers (13 male and 3 female) participated in five different warm-ups in a randomised order prior to the performance of two jumping tests. The warm-ups were control, 4 min run, static stretch, run + stretch, and run + stretch + practice jumps. After a 2 min rest, a concentric jump and a drop jump were performed, which yielded 6 variables expressing fast force production and jumping performance of the leg extensor muscles (concentric jump height, peak force, rate of force developed, drop jump height, contact time and height/time).



Generally the stretching warm-up produced the lowest values and the run or run + stretch + jumps warm-ups produced the highest values of explosive force production. There were no significant differences (p<0.05) between the control and run + stretch warm-ups, whereas the run yielded significantly better scores than the run + stretch warm-up for drop jump height (3.2%), concentric jump height (3.4%) and peak concentric force (2.7%) and rate of force developed (15.4%).



The results indicated that submaximum running and practice jumps had a positive effect whereas static stretching had a negative influence on explosive force and jumping performance. It was suggested that an alternative for static stretching should be considered in warm-ups prior to power activities.
Learning Motion Patterns in Videos

The problem of determining whether an object is in motion, irrespective of camera motion, is far from being solved. We address this challenging task by learning motion patterns in videos. The core of our approach is a fully convolutional network, which is learned entirely from synthetic video sequences, and their ground-truth optical flow and motion segmentation. This encoder-decoder style architecture first learns a coarse representation of the optical flow field features, and then refines it iteratively to produce motion labels at the original high-resolution. We further improve this labeling with an objectness map and a conditional random field, to account for errors in optical flow, and also to focus on moving things rather than stuff. The output label of each pixel denotes whether it has undergone independent motion, i.e., irrespective of camera motion. We demonstrate the benefits of this learning framework on the moving object segmentation task, where the goal is to segment all objects in motion. Our approach outperforms the top method on the recently released DAVIS benchmark dataset, comprising real-world sequences, by 5.6%. We also evaluate on the Berkeley motion segmentation database, achieving state-of-the-art results.
Automatic recognition of fingerspelled words in British Sign Language

We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other, and contains signs which are ambiguous from the observer's viewpoint. The main contributions of our work include: (i) recognition based on hand shape alone, not requiring motion cues; (ii) robust visual features for hand shape recognition; (iii) scalability to large lexicon recognition with no re-training. We report results on a dataset of 1,000 low quality webcam videos of 100 words. The proposed method achieves a word recognition accuracy of 98.9%.
An improved NSGA-III procedure for evolutionary many-objective optimization

Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection pressure based on Pareto-dominance degrades severely with the number of objectives increasing. Very recently, a reference-point based NSGA-II, referred as NSGA-III, is suggested to deal with many-objective problems, where the maintenance of diversity among population members is aided by supplying and adaptively updating a number of well-spread reference points. However, NSGA-III still relies on Pareto-dominance to push the population towards Pareto front (PF), leaving room for the improvement of its convergence ability. In this paper, an improved NSGA-III procedure, called θ-NSGA-III, is proposed, aiming to better tradeoff the convergence and diversity in many-objective optimization. In θ-NSGA-III, the non-dominated sorting scheme based on the proposed θ-dominance is employed to rank solutions in the environmental selection phase, which ensures both convergence and diversity. Computational experiments have shown that θ-NSGA-III is significantly better than the original NSGA-III and MOEA/D on most instances no matter in convergence and overall performance.
Double-ridged Horn Antenna with Sinusoidal Ridge Profile

FDTD numerical study compared to the results of measurement is reported for double-ridged horn antenna with sinusoidal profile of the ridge. Different transitions from coaxial to double-ridged waveguide were considered on the preliminary step of the study. Next, a suitable configuration for feeding the ridges of antenna was chosen. The sinusoidal ridge taper is described in the next part of the paper. Finally, the simulations results of complete antenna are presented. Theoretical characteristics of reflection and antenna patterns are compared to the results of measurements showing acceptable accordance.
Extracting general-purpose features from LIDAR data

The detection of features from Light Detection and Ranging (LIDAR) data is a fundamental component of feature-based mapping and SLAM systems. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be very poor. We describe a general purpose feature detector for LIDAR data that is applicable to virtually any environment. Our methods adapt classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. Our resulting method is capable of identifying stable features at a variety of spatial scales and produces uncertainty estimates for use in a state estimation algorithm. We present results on standard datasets, including Victoria Park and Intel Research Center (both 2D), and the MIT DARPA Urban Challenge dataset (3D).
Image-based rendering in the gradient domain

We propose a novel image-based rendering algorithm for handling complex scenes that may include reflective surfaces. Our key contribution lies in treating the problem in the gradient domain. We use a standard technique to estimate scene depth, but assign depths to image gradients rather than pixels. A novel view is obtained by rendering the horizontal and vertical gradients, from which the final result is reconstructed through Poisson integration using an approximate solution as a data term. Our algorithm is able to handle general scenes including reflections and similar effects without explicitly separating the scene into reflective and transmissive parts, as required by previous work. Our prototype renderer is fully implemented on the GPU and runs in real time on commodity hardware.
HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network

Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for MANETs. Swarm intelligence is a computational intelligence technique that involves collective behavior of autonomous agents that locally interact with each other in a distributed environment to solve a given problem in the hope of finding a global solution to the problem. In this paper, we propose a hybrid routing algorithm for MANETs based on ACO and zone routing framework of bordercasting. The algorithm, HOPNET, based on ants hopping from one zone to the next, consists of the local proactive route discovery within a node’s neighborhood and reactive communication between the neighborhoods. The algorithm has features extracted from ZRP and DSR protocols and is simulated on GlomoSim and is compared to AODV routing protocol. The algorithm is also compared to the well known hybrid routing algorithm, AntHocNet, which is not based on zone routing framework. Results indicate that HOPNET is highly scalable for large networks compared to AntHocNet. The results also indicate that the selection of the zone radius has considerable impact on the delivery packet ratio and HOPNET performs significantly better than AntHocNet for high and low mobility. The algorithm has been compared to random way point model and random drunken model and the results show the efficiency and inefficiency of bordercasting. Finally, HOPNET is compared to ZRP and the strength of nature-inspired algorithm
A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation

Event-based social networks (EBSNs), in which organizers publish events to attract other users in local city to attend offline, emerge in recent years and grow rapidly. Due to the large volume of events in EBSNs, event recommendation is essential. A few recent works focus on this task, while almost all the methods need that each event to be recommended should have been registered by some users to attend. Thus they ignore two essential characteristics of events in EBSNs: (1) a large number of new events will be published every day which means many events have few participants in the beginning, (2) events have life cycles which means outdated events should not be recommended. Overall, event recommendation in EBSNs inevitably faces the cold-start problem.
 In this work, we address the new problem of cold-start local event recommendation in EBSNs. We propose a collective Bayesian Poisson factorization (CBPF) model for handling this problem. CBPF takes recently proposed Bayesian Poisson factorization as its basic unit to model user response to events, social relation, and content text separately. Then it further jointly connects these units by the idea of standard collective matrix factorization model. Moreover, in our model event textual content, organizer, and location information are utilized to learn representation of cold-start events for predicting user response to them. Besides, an efficient coordinate ascent algorithm is adopted to learn the model. We conducted comprehensive experiments on real datasets crawled from EBSNs and the results demonstrate our proposed model is effective and outperforms several alternative methods.