nils-herrmann/clean_scirepeval huggingface dataset:
Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays

Vision-based research for intelligent vehicles have traditionally focused on specific regions around a vehicle, such as a front looking camera for, e.g., lane estimation. Traffic scenes are complex and vital information could be lost in unobserved regions. This paper proposes a framework that uses four visual sensors for a full surround view of a vehicle in order to achieve an understanding of surrounding vehicle behaviors. The framework will assist the analysis of naturalistic driving studies by automating the task of data reduction of the observed trajectories. To this end, trajectories are estimated using a vehicle detector together with a multiperspective optimized tracker in each view. The trajectories are transformed to a common ground plane, where they are associated between perspectives and analyzed to reveal tendencies around the ego-vehicle. The system is tested on sequences from 2.5 h of drive on US highways. The multiperspective tracker is tested in each view as well as for the ability to associate vehicles bet-ween views with a 92% recall score. A case study of vehicles approaching from the rear shows certain patterns in behavior that could potentially influence the ego-vehicle.
Face Search at Scale: 80 Million Gallery

Due to the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to process and search for persons of interest among the billions of shared photos on these websites. Facebook revealed in a 2013 white paper that its users have uploaded more than 250 billion photos, and are uploading 350 million new photos each day. Due to this humongous amount of data, large-scale face search for mining web images is both important and challenging. Despite significant progress in face recognition, searching a large collection of unconstrained face images has not been adequately addressed. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top-k most similar faces using deep features generated from a convolutional neural network. The k retrieved candidates are re-ranked by combining similarities from deep features and the COTS matcher. We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images. Experimental results demonstrate that the deep features are competitive with state-of-the-art methods on unconstrained face recognition benchmarks (LFW and IJB-A). More specifically, on the LFW database, we achieve 98.23% accuracy under the standard protocol and a verification rate of 87.65% at FAR of 0.1% under the BLUFR protocol. For the IJB-A benchmark, our accuracies are as follows: TAR of 51.4% at FAR of 0.1% (verification); Rank 1 retrieval of 82.0% (closed-set search); FNIR of 61.7% at FPIR of 1% (open-set search). Further, the proposed face search system offers an excellent trade-off between accuracy and scalability on datasets consisting of millions of images. Additionally, in an experiment involving searching for face images of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed cascade face search system could find the younger brother’s (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a 5M gallery and at rank 8 in 7 seconds
QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review

As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource management in cloud computing and its techniques are described as developed by various industry and academic groups. Further, taxonomy of autonomic resource management in the cloud has been presented. This research work will help researchers find the important characteristics of autonomic resource management and will also help to select the most suitable technique for autonomic resource management in a specific application along with significant future research directions.
A fingerprint sensor based on the feedback capacitive sensing scheme

This paper introduces a single-chip, 200 200element sensor array implemented in a standard two-metal digital CMOS technology. The sensor is able to grab the fingerprint pattern without any use of optical and mechanical adaptors. Using this integrated sensor, the fingerprint is captured at a rate of 10 F/s by pressing the finger skin onto the chip surface. The fingerprint pattern is sampled by capacitive sensors that detect the electric field variation induced by the skin surface. Several design issues regarding the capacitive sensing problem are reported and the feedback capacitive sensing scheme (FCS) is introduced. More specifically, the problem of the charge injection in MOS switches has been revisited for charge amplifier design.
Mash: fast genome and metagenome distance estimation using MinHash

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( https://github.com/marbl/mash ).
Bayesian calibration of stochastic computer models

Computer models are widely used to simulate real processes. Within the computer model, there always exist some parameters which are unobservable in the real process but need to be specified in the computer model. The procedure to adjust these unknown parameters in order to fit the model to observed data and improve its predictive capability is known as calibration. In traditional calibration, once the optimal calibration parameter set is obtained, it is treated as known for future prediction. Calibration parameter uncertainty introduced from estimation is not accounted for. We will present a Bayesian calibration approach for stochastic computer models. We account for these additional uncertainties and derive the predictive distribution for the real process. Two numerical examples are used to illustrate the accuracy of the proposed method.
Effects of a short-term pre-season training programme on the body composition and anaerobic performance of professional rugby union players.

Pre-season rugby training develops the physical requisites for competition and consists of a high volume of resistance training and anaerobic and aerobic conditioning. However, the effects of a rugby union pre-season in professional athletes are currently unknown. Therefore, the purpose of this investigation was to determine the effects of a 4-week pre-season on 33 professional rugby union players. Bench press and box squat increased moderately (13.6 kg, 90% confidence limits +/-2.9 kg and 17.6 +/- 8.0 kg, respectively) over the training phase. Small decreases in bench throw (70.6 +/- 53.5 W), jump squat (280.1 +/- 232.4 W), and fat mass (1.4 +/- 0.4 kg) were observed. In addition, small increases were seen in fat-free mass (2.0 +/- 0.6 kg) and flexed upper-arm girth (0.6 +/- 0.2 cm), while moderate increases were observed in mid-thigh girth (1.9 +/- 0.5 cm) and perception of fatigue (0.6 +/- 0.4 units). Increases in strength and body composition were observed in elite rugby union players after 4 weeks of intensive pre-season training, but this may have been the result of a return to fitness levels prior to the off-season. Decreases in power may reflect high training volumes and increases in perceived of fatigue.
GelClust: A software tool for gel electrophoresis images analysis and dendrogram generation

This paper presents GelClust, a new software that is designed for processing gel electrophoresis images and generating the corresponding phylogenetic trees. Unlike the most of commercial and non-commercial related softwares, we found that GelClust is very user-friendly and guides the user from image toward dendrogram through seven simple steps. Furthermore, the software, which is implemented in C# programming language under Windows operating system, is more accurate than similar software regarding image processing and is the only software able to detect and correct gel 'smile' effects completely automatically. These claims are supported with experiments.
Speaker-Dependent WaveNet Vocoder

In this study, we propose a speaker-dependent WaveNet vocoder, a method of synthesizing speech waveforms with WaveNet, by utilizing acoustic features from existing vocoder as auxiliary features of WaveNet. It is expected that WaveNet can learn a sample-by-sample correspondence between speech waveform and acoustic features. The advantage of the proposed method is that it does not require (1) explicit modeling of excitation signals and (2) various assumptions, which are based on prior knowledge specific to speech. We conducted both subjective and objective evaluation experiments on CMUARCTIC database. From the results of the objective evaluation, it was demonstrated that the proposed method could generate high-quality speech with phase information recovered, which was lost by a mel-cepstrum vocoder. From the results of the subjective evaluation, it was demonstrated that the sound quality of the proposed method was significantly improved from mel-cepstrum vocoder, and the proposed method could capture source excitation information more accurately.
EFFECTS OF WORD-OF-MOUTH VERSUS TRADITIONAL MARKETING : FINDINGS FROM AN INTERNET SOCIAL NETWORKING SITE

The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations sent out by existing members, outbound WOM may be precisely tracked. WOM, along with traditional marketing, can then be linked to the number of new members subsequently joining the site (signups). Due to the endogeneity among WOM, new signups, and traditional marketing activity, the authors employ a Vector Autoregression (VAR) modeling approach. Estimates from the VAR model show that word-ofmouth referrals have substantially longer carryover effects than traditional marketing actions. The long-run elasticity of signups with respect to WOM is estimated to be 0.53 (substantially larger than the average advertising elasticities reported in the literature) and the WOM elasticity is about 20 times higher than the elasticity for marketing events, and 30 times that of media appearances. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper bound estimate for the financial incentives the firm might offer to stimulate word-of-mouth.
Solitary cutaneous metastasis as the first sign of relapse of thyroid carcinoma: a clinical, dermoscopic-pathologic case study.

Cutaneous metastases are a rare event, representing 0.7% to 2.0% of all cutaneous malignant neoplasms. They may be the first sign of a previously undiagnosed visceral malignancy or the initial presentation of a recurrent neoplasm. The frequency of cutaneous metastases according to the type of underlying malignancies varies with sex. In men, the most common internal malignancies leading to cutaneous metastases are lung cancer, colon cancer, melanoma, squamous cell carcinoma of the oral cavity, and renal cell carcinoma. In women, breast cancer, colon cancer, melanoma, lung cancer, and ovarian cancer are the most common malignancies leading to cutaneous metastases.
Real-time general object recognition for indoor robot based on PCL

We present a system for real-time general object recognition (gor) for indoor robot in complex scenes. A point cloud image containing the object to be recognized from a Kinect sensor, for general object at will, must be extracted a point cloud model of the object with the Cluster Extraction method, and then we can compute the global features of the object model, making up the model database after processing many frame images. Here the global feature we used is Clustered Viewpoint Feature Histogram (CVFH) feature from Point Cloud Library (PCL). For real-time gor we must preprocess all the point cloud images streamed from the Kinect into clusters based on a clustering threshold and the min-max cluster sizes related to the size of the model, for reducing the amount of the clusters and improving the processing speed, and also compute the CVFH features of the clusters. For every cluster of a frame image, we search the several nearer features from the model database with the KNN method in the feature space, and we just consider the nearest model. If the strings of the model name contain the strings of the object to be recognized, it can be considered that we have recognized the general object; otherwise, we compute another cluster again and perform the above steps. The experiments showed that we had achieved the real-time recognition, and ensured the speed and accuracy for the gor.
Update Summarization via Graph-Based Sentence Ranking

Due to the fast evolution of the information on the Internet, update summarization has received much attention in recent years. It is to summarize an evolutionary document collection at current time supposing the users have read some related previous documents. In this paper, we propose a graph-ranking-based method. It performs constrained reinforcements on a sentence graph, which unifies previous and current documents, to determine the salience of the sentences. The constraints ensure that the most salient sentences in current documents are updates to previous documents. Since this method is NP-hard, we then propose its approximate method, which is polynomial time solvable. Experiments on the TAC 2008 and 2009 benchmark data sets show the effectiveness and efficiency of our method.
Make it stand: balancing shapes for 3D fabrication

Imbalance suggests a feeling of dynamism and movement in static objects. It is therefore not surprising that many 3D models stand in impossibly balanced configurations. As long as the models remain in a computer this is of no consequence: the laws of physics do not apply. However, fabrication through 3D printing breaks the illusion: printed models topple instead of standing as initially intended. We propose to assist users in producing novel, properly balanced designs by interactively deforming an existing model. We formulate balance optimization as an energy minimization, improving stability by modifying the volume of the object, while preserving its surface details. This takes place during interactive editing: the user cooperates with our optimizer towards the end result. We demonstrate our method on a variety of models. With our technique, users can produce fabricated objects that stand in one or more surprising poses without requiring glue or heavy pedestals.
Tezos : A Self-Amending Crypto-Ledger Position Paper

The popularization of Bitcoin, a decentralized crypto-currency has inspired the production of several alternative, or “alt”, currencies. Ethereum, CryptoNote, and Zerocash all represent unique contributions to the cryptocurrency space. Although most alt currencies harbor their own source of innovation, they have no means of adopting the innovations of other currencies which may succeed them. We aim to remedy the potential for atrophied evolution in the crypto-currency space by presenting Tezos, a generic and self-amending crypto-ledger. Tezos can instantiate any blockchain based protocol. Its seed protocol specifies a procedure for stakeholders to approve amendments to the protocol, including amendments to the amendment procedure itself. Upgrades to Tezos are staged through a testing environment to allow stakeholders to recall potentially problematic amendments. The philosophy of Tezos is inspired by Peter Suber’s Nomic[1], a game built around a fully introspective set of rules. In this paper, we hope to elucidate the potential benefits of Tezos, our choice to implement as a proof-of-stake system, and our choice to write it
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences

In this paper we explore the problem of fitting a 3D morphable model to single face images using only sparse geometric features (edges and landmark points). Previous approaches to this problem are based on nonlinear optimisation of an edge-derived cost that can be viewed as forming soft correspondences between model and image edges. We propose a novel approach, that explicitly computes hard correspondences. The resulting objective function is non-convex but we show that a good initialisation can be obtained efficiently using alternating linear least squares in a manner similar to the iterated closest point algorithm. We present experimental results on both synthetic and real images and show that our approach outperforms methods that use soft correspondence and other recent methods that rely solely on geometric features.
Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands

In this paper we present a framework for grasp planning with a humanoid robot arm and a five-fingered hand. The aim is to provide the humanoid robot with the ability of grasping objects that appear in a kitchen environment. Our approach is based on the use of an object model database that contains the description of all the objects that can appear in the robot workspace. This database is completed with two modules that make use of this object representation: an exhaustive offline grasp analysis system and a real-time stereo vision system. The offline grasp analysis system determines the best grasp for the objects by employing a simulation system, together with CAD models of the objects and the five-fingered hand. The results of this analysis are added to the object database using a description suited to the requirements of the grasp execution modules. A stereo camera system is used for a real-time object localization using a combination of appearance-based and model-based methods. The different components are integrated in a controller architecture to achieve manipulation task goals for the humanoid robot
Unrolled Generative Adversarial Networks

We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator’s objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.
Automatic summarising: a review and discussion of the state of the art

This paper reviews research on automatic summarising over the last decade. This period has seen a rapid growth of work in the area stimulated by technology and by several system evaluation programmes. The review makes use of several frameworks to organise the review, for summarising, for systems, for the task factors affecting summarising, and for evaluation design and practice. The review considers the evaluation strategies that have been applied to summarising and the issues they raise, and the major summary evaluation programmes. It examines the input, purpose and output factors that have been investigated in summarising research in the last decade, and discusses the classes of strategy, both extractive and non-extractive, that have been explored, illustrating the range of systems that have been built. This analysis of strategies is amplified by accounts of specific exemplar systems. The conclusions drawn from the review are that automatic summarisation research has made valuable progress in the last decade, with some practically useful approaches, better evaluation, and more understanding of the task. However as the review also makes clear, summarising systems are often poorly motivated in relation to the factors affecting summaries, and evaluation needs to be taken significantly further so as to engage with the purposes for which summaries are intended and the contexts in which they are used. A reduced version of this report, entitled ‘Automatic summarising: the state of the art’ will appear in Information Processing and Management, 2007.
HDTrans: a low-overhead dynamic translator

Dynamic translation is a general purpose tool used for instrumenting programs at run time. Many current translators perform substantial rewriting during translation in an attempt to reduce execution time. When dynamic translation is used as a ubiquitous policy enforcement mechanism, the majority of program executions have no dominating inner loop that can be used to amortize the cost of translation. Even under more favorable usage assumptions, our measurements show that such optimizations offer no significant benefit in most cases. A simpler, more maintainable, adaptable, and smaller translator may be preferable to more complicated designs.
 In this paper, we present HDTrans, a light-weight IA-32 to IA-32 binary translation system that uses some simple and effective translation techniques in combination with established trace linearization and code caching optimizations. We also present an evaluation of translation overhead under non-ideal conditions, showing that conventional benchmarks do not provide a good prediction of translation overhead when used pervasively.
 A further contribution of this paper is an analysis of the effectiveness of post-compile static pre-translation techniques for overhead reduction. Our results indicate that static pre-translation is effective only when expensive instrumentation or optimization is performed, and that efficient reload of pre-translated code incurs a substantial execution-time penalty.