nils-herrmann/clean_scirepeval huggingface dataset:
Event Detection and Analysis from Video Streams

We present a system for event detection and analysis from video streams. Our approach is based on a detection and tracking module which extracts moving objects trajectories from a video stream. These trajectories, together with a rough description of the scene, are then used by the behavior inference module in order to recognize and classify object motion. The hierarchical tasks are performed on a buffered set of frames in order to provide accurate results by taking into account the temporal coherence of moving objects.
A method for cartoon-style rendering of liquid animations

In this paper we present a visually compelling and informative cartoon rendering style for liquid animations. Our style is inspired by animations such as Futurama,1 The Little Mermaid,2 and Bambi2. We take as input a liquid surface obtained from a three-dimensional physically based liquid simulation system and output animations that evoke a cartoon style and convey liquid movement. Our method is based on four cues that emphasize properties of the liquid's shape and motion. We use bold outlines to emphasize depth discontinuities, patches of constant color to highlight near-silhouettes and areas of thinness, and, optionally place temporally coherent oriented textures on the liquid surface to help convey motion.
A Dark Side of the Cannula Injections: How Arterial Wall Perforations and Emboli Occur

Though most injectors prefer to use a cannula rather than a needle, there have been reported cases of blindness following cannula injections. This study investigated possible situations in which a cannula can injure an artery to gain more insight about the vascular complication and its prevention. To understand how an arterial injury occurs, five situations favoring vascular injury were simulated and tested. Experiments were performed in 100 arterial segments of 10 soft embalmed cadavers with red latex injections to the arteries. The frontal branch of the superficial temporal artery with a diameter between 1.2 and 1.5 mm was chosen for the experiment with a 25G cannula. Five situations were created to simulate any possibility that the cannula can penetrate through the arterial wall. Two factors were varied for simulation of specific danger situations. Factors that vary were as follows: (1) the angles between the cannula and the artery when the cannula touched the artery, and (2) the segments of the artery with different features. The cannula could penetrate the arterial wall in some specific situations with a different chance in each situation. The perpendicular angle between the artery and the cannula was one of the essential situations for vascular injury. Situations that had a similar effect of the perpendicular arterial surface related to the cannula axis also favored vascular injuries. During a blinded insertion of cannula injections to reach the target area, the injector cannot discriminate the sensation at the cannula tip between the resistance of a fibrous septum in the way of the insertion and the resistance of encountering an artery. To prevent arterial emboli, the cannula trajectory should not be close to the main artery in the region. This allows a physician to safely perform an intermittent forceful insertion without an arterial injury during an attempt to perform a gentle cannula insertion. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors. www.springer.com/00266 .
Development of an underwater gravity measurement system using autonomous underwater vehicle for exploration of seafloor deposits

It is known that there are mineral deposits below seafloor. Gravity survey is one of powerful methods for exploration of underground deposits. A new system is needed to survey a wide area quickly with a higher resolution. Recent technology of an autonomous underwater vehicle (AUV) enables us to measure gravity in underwater environment. We have developed an underwater gravity measurement system for exploration below a seafloor using an AUV. Our system consists of an underwater gravimeter and an underwater gravity gradiometer. The resolution of approximately 0.1 mgal for gravity and 10 Eötvös for gradient is required for the seafloor exploration. For the gravimeter system, we adopted an improved sea/air gravimeter and the sensor is mounted on a gimbal mechanism to keep vertical. A pressure capsule for the gravimeter has a diameter of 50 cm. The gradiometer has two gravimeters aligned vertical at a distance of 44 cm. We developed a gravimeter itself for the gradiometer. Both gravimeters are contained in a vacuum capsule and a gimbal system is also used to keep them vertical. For practical observation in the sea, we choose a large AUV Urashima. All the power is supplied from the Urashima and acoustic communication system on the Urashima enable real-time monitoring during observation. The first observation was carried out in September 2012 in Sagami-Bay, Japan. The Urashima was navigated at a constant speed and constant depth on the same profile. We succeeded in obtaining gravity data and it is found that our system has an accuracy or repeatability of 0.1 mgal for the gravity measurement. In August 2014, we carried out the second gravity survey using our underwater gravity measurement system in Izena caldera, the middle Okinawa Trough, where seafloor deposits were found. We obtained the data from both gravimeter and gradiometer with good quality for all the profiles.
Multiparty Computation from Somewhat Homomorphic Encryption

We propose a general multiparty computation protocol secure against an active adversary corrupting up to n−1 of the n players. The protocol may be used to compute securely arithmetic circuits over any finite field Fpk . Our protocol consists of a preprocessing phase that is both independent of the function to be computed and of the inputs, and a much more efficient online phase where the actual computation takes place. The online phase is unconditionally secure and has total computational (and communication) complexity linear in n, the number of players, where earlier work was quadratic in n. Moreover, the work done by each player is only a small constant factor larger than what one would need to compute the circuit in the clear. We show this is optimal for computation in large fields. In practice, for 3 players, a secure 64-bit multiplication can be done in 0.05 ms. Our preprocessing is based on a somewhat homomorphic cryptosystem. We extend a scheme by Brakerski et al., so that we can perform distributed decryption and handle many values in parallel in one ciphertext. The computational complexity of our preprocessing phase is dominated by the public-key operations, we need O(n/s) operations per secure multiplication where s is a parameter that increases with the security parameter of the cryptosystem. Earlier work in this model needed Ω(n) operations. In practice, the preprocessing prepares a secure 64-bit multiplication for 3 players in about 13 ms.
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

Suppose we are given a vector f in a class FsubeRopf<sup>N </sup>, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision epsi in the Euclidean (lscr<sub>2</sub>) metric? This paper shows that if the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program. More precisely, suppose that the nth largest entry of the vector |f| (or of its coefficients in a fixed basis) obeys |f|<sub>(n)</sub>lesRmiddotn<sup>-1</sup>p/, where R>0 and p>0. Suppose that we take measurements y<sub>k</sub>=langf<sup># </sup>,X<sub>k</sub>rang,k=1,...,K, where the X<sub>k</sub> are N-dimensional Gaussian vectors with independent standard normal entries. Then for each f obeying the decay estimate above for some 0<p<1 and with overwhelming probability, our reconstruction f<sup>t</sup>, defined as the solution to the constraints y<sub>k</sub>=langf<sup># </sup>,X<sub>k</sub>rang with minimal lscr<sub>1</sub> norm, obeys parf-f<sup>#</sup>par<sub>lscr2</sub>lesC<sub>p </sub>middotRmiddot(K/logN)<sup>-r</sup>, r=1/p-1/2. There is a sense in which this result is optimal; it is generally impossible to obtain a higher accuracy from any set of K measurements whatsoever. The methodology extends to various other random measurement ensembles; for example, we show that similar results hold if one observes a few randomly sampled Fourier coefficients of f. In fact, the results are quite general and require only two hypotheses on the measurement ensemble which are detailed
Grouplet: A structured image representation for recognizing human and object interactions

Psychologists have proposed that many human-object interaction activities form unique classes of scenes. Recognizing these scenes is important for many social functions. To enable a computer to do this is however a challenging task. Take people-playing-musical-instrument (PPMI) as an example; to distinguish a person playing violin from a person just holding a violin requires subtle distinction of characteristic image features and feature arrangements that differentiate these two scenes. Most of the existing image representation methods are either too coarse (e.g. BoW) or too sparse (e.g. constellation models) for performing this task. In this paper, we propose a new image feature representation called “grouplet”. The grouplet captures the structured information of an image by encoding a number of discriminative visual features and their spatial configurations. Using a dataset of 7 different PPMI activities, we show that grouplets are more effective in classifying and detecting human-object interactions than other state-of-the-art methods. In particular, our method can make a robust distinction between humans playing the instruments and humans co-occurring with the instruments without playing.
Alternative Splicing: New Insights from Global Analyses

Recent analyses of sequence and microarray data have suggested that alternative splicing plays a major role in the generation of proteomic and functional diversity in metazoan organisms. Efforts are now being directed at establishing the full repertoire of functionally relevant transcript variants generated by alternative splicing, the specific roles of such variants in normal and disease physiology, and how alternative splicing is coordinated on a global level to achieve cell- and tissue-specific functions. Recent progress in these areas is summarized in this review.
A Survey of Homomorphic Encryption for Nonspecialists

The goal of encryption is to ensure confidentiali y of data in communication and storage processes. Recently, its use in constrained devices led to consider additional features, such as the ability to delegate computations to untrusted computers. For this purpose, we would like to give the untrusted computer only an encrypted version of the data to process. The computer will perform the computation on this encrypted data, hence without knowing anything on its real value. Finally, it will send back the result, and we will decrypt it. For coherence, the decrypted result has to be equal to the intended computed value if performed on the original data. For this reason, the encryption scheme has to present a particular structure. Rivest et al. proposed in 1978 to solve this issue through homomorphic encryption . Unfortunately, Brickell and Yacobi pointed out in  some security f aws in the firs proposals of Rivest et al. Since this f rst attempt, a lot of articles have proposed solutions dedicated to numerous application contexts: secret sharing schemes, threshold schemes (see, e.g., ), zero-knowledge proofs (see, e.g., ), oblivious transfer (see, e.g., ), commitment schemes (see, e.g., ), anonymity, privacy, electronic voting, electronic auctions, lottery protocols (see, e.g., ), protection ofmobile agents (see, e.g., ), multiparty computation (see, e.g., ), mix-nets (see, e.g., [8, 9]), watermarking or finge printing protocols (see, e.g., [10–14]), and so forth. The goal of this article is to provide nonspecialists with a survey of homomorphic encryption techniques. Section 2 recalls some basic concepts of cryptography and presents homomorphic encryption; it is particularly aimed at noncryptographers, providing guidelines about the main characteristics of encryption primitives: algorithms, performance, security. Section 3 provides a survey of homomorphic encryption schemes published so far, and analyses their characteristics. Most schemes we describe are based onmathematical notions the reader may not be familiar with. In the cases these notions can easily be introduced, we present them briefl . The reader may refer to  for more information concerning those we could not introduce properly, or algorithmic problems related to their computation. Before going deeper in the subject, let us introduce some notation. The integer (x) denotes the number of bits constituting the binary expansion of x. As usual, Zn will denote the set of integers modulo n, and Z∗n the set of its invertible elements.
Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures

In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.
Compact query term selection using topically related text

Many recent and highly effective retrieval models for long queries use query reformulation methods that jointly optimize term weights and term selection. These methods learn using word context and global context but typically fail to capture query context. In this paper, we present a novel term ranking algorithm, PhRank, that extends work on Markov chain frameworks for query expansion to select compact and focused terms from within a query itself. This focuses queries so that one to five terms in an unweighted model achieve better retrieval effectiveness than weighted term selection models that use up to 30 terms. PhRank terms are also typically compact and contain 1-2 words compared to competing models that use query subsets up to 7 words long. PhRank captures query context with an affinity graph constructed using word co-occurrence in pseudo-relevant documents. A random walk of the graph is used for term ranking in combination with discrimination weights. Empirical evaluation using newswire and web collections demonstrates that performance of reformulated queries is significantly improved for long queries and at least as good for short, keyword queries compared to highly competitive information retrieval (IR) models.
“Diagnosis and classification of grape leaf diseases using neural networks”

Plant diseases cause significant damage and economic losses in crops. Subsequently, reduction in plant diseases by early diagnosis results in substantial improvement in quality of the product. Erroneous diagnosis of disease and its severity leads to inappropriate use of pesticides. The goal of proposed work is to diagnose the disease using image processing and artificial intelligence techniques on images of grape plant leaf. In the proposed system, grape leaf image with complex background is taken as input. Thresholding is deployed to mask green pixels and image is processed to remove noise using anisotropic diffusion. Then grape leaf disease segmentation is done using K-means clustering. The diseased portion from segmented images is identified. Best results were observed when Feed forward Back Propagation Neural Network was trained for classification.
'C and tcc: A Language and Compiler for Dynamic Code Generation

Dynamic code generation allows programmers to use run-time information in order to achieve performance and expressiveness superior to those of static code. The 'C(Tick C) language is a superset of ANSI C that supports efficient and high-level use of dynamic code generation. 'C provides dynamic code generation at the level of C expressions and statements and supports the composition of dynamic code at run time. These features enable programmers to add dynamic code generation to existing C code incrementally and to write important applications (such as “just-in-time” compilers) easily. The article presents many examples of how 'C can be used to solve practical problems. The tcc compiler is an efficient, portable, and freely available implementation of 'C. tcc allows programmers to trade dynamic compilation speed for dynamic code quality: in some aplications, it is most important to generate code quickly, while in others code quality matters more than compilation speed. The overhead of dynamic compilation is on the order of 100 to 600 cycles per generated instruction, depending on the level of dynamic optimizaton. Measurements show that the use of dynamic code generation can improve performance by almost an order of magnitude; two- to four-fold speedups are common. In most cases, the overhead of dynamic compilation is recovered in under 100 uses of the dynamic code; sometimes it can be recovered within one use.
Combining Poisson singular integral and total variation prior models in image restoration

In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods. & 2013 Elsevier B.V. All rights reserved.
FeUdal Networks for Hierarchical Reinforcement Learning

We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels – allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits – in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation. We demonstrate the performance of our proposed system on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment.
E-Cash from Bitcoin

Bitcoin is the first e-cash system to see widespread adoption. While Bitcoin offers the potential for new types of financial interaction, it has significant limitations regarding privacy. Specifically, because the Bitcoin transaction log is completely public, users’ privacy is protected only through the use of pseudonyms. In this paper we propose Zerocoin, a cryptographic extension to Bitcoin that augments the protocol to allow for fully anonymous currency transactions. Our system uses standard cryptographic assumptions and does not introduce new trusted parties or otherwise change the security model of Bitcoin. We detail Zerocoin’s cryptographic construction, its integration into Bitcoin, and examine its performance both in terms of computation and impact on the Bitcoin protocol.
Learning Extractors from Unlabeled Text using Relevant Databases

Supervised machine learning algorithms for information extraction generally require large amounts of training data. In many cases where labeling training data is burdensome, there may, however, already exist an incomplete database relevant to the task at hand. Records from this database can be used to label text strings that express the same information. For tasks where text strings do not follow the same format or layout, and additionally may contain extra information, labeling the strings completely may be problematic. This paper presents a method for training extractors which fill in missing labels of a text sequence that is partially labeled using simple high-precision heuristics. Furthermore, we improve the algorithm by utilizing labeled fields from the database. In experiments with BibTeX records and research paper citation strings, we show a significant improvement in extraction accuracy over a baseline that only relies on the database for training data.
Generating performance portable code using rewrite rules: from high-level functional expressions to high-performance OpenCL code

Computers have become increasingly complex with the emergence of heterogeneous hardware combining multicore CPUs and GPUs. These parallel systems exhibit tremendous computational power at the cost of increased programming effort resulting in a tension between performance and code portability. Typically, code is either tuned in a low-level imperative language using hardware-specific optimizations to achieve maximum performance or is written in a high-level, possibly functional, language to achieve portability at the expense of performance. We propose a novel approach aiming to combine high-level programming, code portability, and high-performance. Starting from a high-level functional expression we apply a simple set of rewrite rules to transform it into a low-level functional representation, close to the OpenCL programming model, from which OpenCL code is generated. Our rewrite rules define a space of possible implementations which we automatically explore to generate hardware-specific OpenCL implementations. We formalize our system with a core dependently-typed lambda-calculus along with a denotational semantics which we use to prove the correctness of the rewrite rules. We test our design in practice by implementing a compiler which generates high performance imperative OpenCL code. Our experiments show that we can automatically derive hardware-specific implementations from simple functional high-level algorithmic expressions offering performance on a par with highly tuned code for multicore CPUs and GPUs written by experts.