nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Background Learning through Deep Auto-encoder Networks

Background learning is a pre-processing of motion detection which is a basis step of video analysis. For the static background, many previous works have already achieved good performance. However, the results on learning dynamic background are still much to be improved. To address this challenge, in this paper, a novel and practical method is proposed based on deep auto-encoder networks. Firstly, dynamic background images are extracted through a deep auto-encoder network (called Background Extraction Network) from video frames containing motion objects. Then, a dynamic background model is learned by another deep auto-encoder network (called Background Learning Network) using the extracted background images as the input. To be more flexible, our background model can be updated on-line to absorb more training samples. Our main contributions are 1) a cascade of two deep auto-encoder networks which can deal with the separation of dynamic background and foregrounds very efficiently; 2) a method of online learning is adopted to accelerate the training of Background Extraction Network. Compared with previous algorithms, our approach obtains the best performance over six benchmark data sets. Especially, the experiments show that our algorithm can handle large variation background very well.
zNLP: Identifying Parallel Sentences in Chinese-English Comparable Corpora

This paper describes the zNLP system for the BUCC 2017 shared task. Our system identifies parallel sentence pairs in Chinese-English comparable corpora by translating word-by-word Chinese sentences into English, using the search engine Solr to select near-parallel sentences and then by using an SVM classifier to identify true parallel sentences from the previous results. It obtains an F1-score of 45% (resp. 43%) on the test (training) set.
A Consolidated Open Knowledge Representation for Multiple Texts

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open textbased manner. We do so by consolidating OIE extractions using entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to give semantic applications an easy handle on consolidated information across multiple texts.
PosDB: A Distributed Column-Store Engine

In this paper we present a novel disk-based distributed column-store, describe its architecture and discuss a number of technical solutions. Our system is essentially a query engine which was written completely from scratch. It is aimed for shared-nothing environments and supports different forms of parallel query processing. Query processing in PosDB is organized according to the classic Volcano pull-based model which is adapted for the column-store case. Currently, we support late materialization only, and therefore employ a join index data structure to represent positional information. In our system query plan can consist of both positional and value operators. PosDB has about a dozen of core operators among which several variants of selections and joins, aggregation. We also have several operators that ensure intra-query parallelism and operators for network interoperability. In its current state the system is fully capable of processing the Star Schema Benchmark in a local and distributed environment.
Nasal Tip Deficiency.

Nasal tip deficiency can be congenital or secondary to previous nasal surgeries. Underdeveloped medial crura usually present with underprojected tip and lack of tip definition. Weakness or malposition of lateral crura causes alar rim retraction and lateral nasal wall weakness. Structural grafting of alar cartilages strengthens the tip framework, reinforces the disrupted support mechanisms, and controls the position of the nasal tip. In secondary cases, anatomic reconstruction of the weakened or interrupted alar cartilages and reconstitution of a stable nasal tip tripod must be the goal for a predictable outcome.
Kernelization of Tensor-Based Models for Multiway Data Analysis: Processing of Multidimensional Structured Data

Tensors (also called multiway arrays) are a generalization of vectors and matrices to higher dimensions based on multilinear algebra. The development of theory and algorithms for tensor decompositions (factorizations) has been an active area of study within the past decade, e.g.,  and . These methods have been successfully applied to many problems in unsupervised learning and exploratory data analysis. Multiway analysis enables one to effectively capture the multilinear structure of the data, which is usually available as a priori information about the data. Hence, it might provide advantages over matrix factorizations by enabling one to more effectively use the underlying structure of the data. Besides unsupervised tensor decompositions, supervised tensor subspace regression and classification formulations have been also successfully applied to a variety of fields including chemometrics, signal processing, computer vision, and neuroscience.
Expressivity of coalgebraic modal logic: The limits and beyond

Modal logic has a good claim to being the logic of choice for describing the reactive behaviour of systems modelled as coalgebras. Logics with modal operators obtained from so-called predicate liftings have been shown to be invariant under behavioural equivalence. Expressivity results stating that, conversely, logically indistinguishable states are behaviourally equivalent depend on the existence of separating sets of predicate liftings for the signature functor at hand. Here, we provide a classification result for predicate liftings which leads to an easy criterion for the existence of such separating sets, and we give simple examples of functors that fail to admit expressive normal or monotone modal logics, respectively, or in fact an expressive (unary) modal logic at all. We then move on to polyadic modal logic, where modal operators may take more than one argument formula. We show that every accessible functor admits an expressive polyadic modal logic. Moreover, expressive polyadic modal logics are, unlike unary modal logics, compositional.
Optimal real-time distributed V2G and G2V management of electric vehicles

Optimal real-time distributed V2G and G2V management of electric vehicles Sonja Stüdli, Emanuele Crisostomi, Richard Middleton & Robert Shorten a Centre for Complex Dynamic Systems and Control, The University of Newcastle, New South Wales, Australia b Department of Energy, Systems, Territory and Constructions, University of Pisa, Pisa, Italy c IBM Research, Dublin, Ireland Accepted author version posted online: 10 Dec 2013.Published online: 05 Feb 2014.
Seeded Graph Matching via Large Neighborhood Statistics

We study a well known noisy model of the graph isomorphism problem. In this model, the goal is to perfectly recover the vertex correspondence between two edge-correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. Specifically, the model first generates a parent graph G0 from Erdős-Rényi random graph G(n, p) and then obtains two children graphs G1 and G2 by subsampling the edge set of G0 twice independently with probability s = Θ(1). The vertex correspondence between G1 and G2 is obscured by randomly permuting the vertex labels of G1 according to a latent permutation π . Finally, for each i, π(i) is revealed independently with probability α as seeds. In the sparse graph regime where np ≤ n for any ǫ < 1/6, we give a polynomial-time algorithm which perfectly recovers π, provided that nps2− logn → +∞ and α ≥ n. This further leads to a sub-exponential-time, exp ( n ) , matching algorithm even without seeds. On the contrary, if nps2−logn = O(1), then perfect recovery is information-theoretically impossible as long as α is bounded away from 1. In the dense graph regime, where np = bn, for fixed constants a, b ∈ (0, 1], we give a polynomial-time algorithm which succeeds when b = O(s) and α = Ω ( (np) logn ) . In particular, when a = 1/k for an integer k ≥ 1, α = Ω(log n/n) suffices, yielding a quasipolynomial-time n algorithm matching the best known algorithm by Barak et al. for the problem of graph matching without seeds when k ≥ 153 and extending their result to new values of p for k = 2, . . . , 152. Unlike previous work on graph matching, which used small neighborhoods or small subgraphs with a logarithmic number of vertices in order to match vertices, our algorithms match vertices if their large neighborhoods have a significant overlap in the number of seeds.
Does intrinsic motivation strengthen physical activity habit? Modeling relationships between self-determination, past behaviour, and habit strength

Habit formation is thought to aid maintenance of physical activity, but little research is available into determinants of habit strength aside from repeated performance. Previous work has shown that intrinsically motivated physical activity, underpinned by inherent satisfaction derived from activity, is more likely to be sustained. We explored whether this might reflect a tendency for self-determined activity to become more strongly habitual. A sample of 192 adults aged 18–30 completed measures of motivational regulation, intention, behaviour, and habit strength. Results showed that self-determined regulation interacted with past behaviour in predicting habit strength: prior action was more predictive of habit strength among more autonomously motivated participants. There was an unexpected direct effect of self-determined regulation on habit strength, independently of past behaviour. Findings offer possible directions for future habit formation work.
Quinoa (Chenopodium quinoa Willd.): composition, chemistry, nutritional, and functional properties.

Quinoa (Chenopodium quinoa Willd.), which is considered a pseudocereal or pseudograin, has been recognized as a complete food due to its protein quality. It has remarkable nutritional properties; not only from its protein content (15%) but also from its great amino acid balance. It is an important source of minerals and vitamins, and has also been found to contain compounds like polyphenols, phytosterols, and flavonoids with possible nutraceutical benefits. It has some functional (technological) properties like solubility, water-holding capacity (WHC), gelation, emulsifying, and foaming that allow diversified uses. Besides, it has been considered an oil crop, with an interesting proportion of omega-6 and a notable vitamin E content. Quinoa starch has physicochemical properties (such as viscosity, freeze stability) which give it functional properties with novel uses. Quinoa has a high nutritional value and has recently been used as a novel functional food because of all these properties; it is a promising alternative cultivar.
I get it already! the influence of ChairBot motion gestures on bystander response

How could a rearranging chair convince you to let it by? This paper explores how robotic chairs might negotiate passage in shared spaces with people, using motion as an expressive cue. The user study evaluates the efficacy of three gestures at convincing a busy participant to let it by. This within-participants study consisted of three subsequent trials, in which a person is completing a puzzle on a standing desk and a robotic chair approaches to squeeze by. The measure was whether participants moved out of the robot's way or not. People deferred to the robot in slightly less than half the trials as they were engaged in the activity. The main finding, however, is that over-communication cues more blocking behaviors, perhaps because it is annoying or because people want chairs to know their place (socially speaking). The Forward-Back gesture that was most effective at negotiating passage in the first trail was least effective in the second and third trial. The more subtle Pause and the slightly loud but less-aggressive Side-to-Side gesture, were much more likely to be deferred to in later trials, but not a single participant deferred to them in the first trial. The results demonstrate that the Forward-Back gesture was the clearest way to communicate the robot's intent, however, they also give evidence that there is a communicative trade-off between clarity and politeness, particularly when direct communication has an association with aggression. The takeaway for robot design is: be informative initially, but avoid over-communicating later.
Quadriceps function assessment using an incremental test and magnetic neurostimulation: a reliability study.

We investigated the reliability of a test assessing quadriceps strength, endurance and fatigability in a single session. We used femoral nerve magnetic stimulation (FMNS) to distinguish central and peripheral factors of neuromuscular fatigue. We used a progressive incremental loading with multiple assessments to limit the influence of subject's cooperation and motivation. Twenty healthy subjects (10 men and 10 women) performed the test on two different days. Maximal voluntary strength and evoked quadriceps responses via FMNS were measured before, after each set of 10 submaximal isometric contractions (5-s on/5-s off; starting at 10% of maximal voluntary strength with 10% increments), immediately and 30min after task failure. The test induced progressive peripheral (41±13% reduction in single twitch at task failure) and central fatigue (3±7% reduction in voluntary activation at task failure). Good inter-day reliability was found for the total number of submaximal contractions achieved (i.e. endurance index: ICC=0.83), for reductions in maximal voluntary strength (ICC>0.81) and evoked muscular responses (i.e. fatigue index: ICC>0.85). Significant sex-differences were also detected. This test shows good reliability for strength, endurance and fatigability assessments. Further studies should be conducted to evaluate its feasibility and reliability in patients.
Combinatorial design of near-optimum masks for coded aperture imaging

In coded aperture imaging the attainable quality of the reconstructed images strongly depends on the choice of the aperture pattern. Optimum mask patterns can be designed from binary arrays with constant sidelobes of their periodic autocorrelation function, the so{called URAs. However, URAs exist for a restricted number of aperture sizes and open fractions only. Using a mismatched lter decoding scheme, artifact{free reconstructions can be obtained even if the aperture array violates the URA condition. A general expression and an upper bound for the signal{to{noise ratio as a function of the aperture array and the relative detector noise level are derived. Combinatorial optimization algorithms, such as the Great Deluge algorithm, are employed for the design of near{optimum aperture arrays. The signal{to{noise ratio of the reconstructions is predicted to be only slightly inferior to the URA case while no restrictions with respect to the aperture size or open fraction are imposed.
NAG: Network for Adversary Generation

Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility constraint to craft the perturbations. However, for a given classifier, they generate one perturbation at a time, which is a single instance from the manifold of adversarial perturbations. Also, in order to build robust models, it is essential to explore the manifold of adversarial perturbations. In this paper, we propose for the first time, a generative approach to model the distribution of adversarial perturbations. The architecture of the proposed model is inspired from that of GANs and is trained using fooling and diversity objectives. Our trained generator network attempts to capture the distribution of adversarial perturbations for a given classifier and readily generates a wide variety of such perturbations. Our experimental evaluation demonstrates that perturbations crafted by our model (i) achieve state-of-the-art fooling rates, (ii) exhibit wide variety and (iii) deliver excellent cross model generalizability. Our work can be deemed as an important step in the process of inferring about the complex manifolds of adversarial perturbations.
The BAF: a corpus of english-french bitext

The BAF is a corpus of English and French translations, hand-aligned at the sentence level, which was developed by the University of Montreal's RALI laboratory, within the "Action de recherche concertée" (ARC) A2, a cooperative research project initiated and financed by the AUPELF-UREF. The corpus, which totals approximately 800 000 words, is primarily intended as an evaluation tool in the development of automatic bilingual text alignment method. In this paper, we discuss why this corpus was assembled, how it was produced, and what it contains. We also describe some of the computer tools that were developed and used in the process.
PrivacyGuard: A VPN-based Platform to Detect Information Leakage on Android Devices

More and more people rely on mobile devices to access the Internet, which also increases the amount of private information that can be gathered from people's devices. Although today's smartphone operating systems are trying to provide a secure environment, they fail to provide users with adequate control over and visibility into how third-party applications use their private data. Whereas there are a few tools that alert users when applications leak private information, these tools are often hard to use by the average user or have other problems. To address these problems, we present PrivacyGuard, an open-source VPN-based platform for intercepting the network traffic of applications. PrivacyGuard requires neither root permissions nor any knowledge about VPN technology from its users. PrivacyGuard does not significantly increase the trusted computing base since PrivacyGuard runs in its entirety on the local device and traffic is not routed through a remote VPN server. We implement PrivacyGuard on the Android platform by taking advantage of the VPNService class provided by the Android SDK.
 PrivacyGuard is configurable, extensible, and useful for many different purposes. We investigate its use for detecting the leakage of multiple types of sensitive data, such as a phone's IMEI number or location data. PrivacyGuard also supports modifying the leaked information and replacing it with crafted data for privacy protection. According to our experiments, PrivacyGuard can detect more leakage incidents by applications and advertisement libraries than TaintDroid. We also demonstrate that PrivacyGuard has reasonable overhead on network performance and almost no overhead on battery consumption.
The Hive: An Edge-based Middleware Solution for Resource Sharing in the Internet of Things

With today's unprecedented proliferation in smart-devices, the Internet of Things Vision has become more of a reality than ever. With the extreme diversity of applications running on these heterogeneous devices, numerous middle-ware solutions have consequently emerged to address IoT-related challenges. These solutions however, heavily rely on the cloud for better data management, integration, and processing. This might potentially compromise privacy, add latency, and place unbearable traffic load. In this paper, we propose The Hive, an edge-based middleware architecture and protocol, that enables heterogeneous edge devices to dynamically share data and resources for enhanced application performance and privacy. We implement a prototype of the Hive, test it for basic robustness, show its modularity, and evaluate its performance with a real world smart emotion recognition application running on edge devices.
Ranking in evolving complex networks

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithmsperform, andwhich are their possible biases thatmay impair their effectiveness. Many popular ranking algorithms (such as Google’s PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks.We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.
Hangable Graph

Let G “ pVG, EGq be a connected graph. The distance dGpu, vq between vertices u and v in G is the length of a shortest u  ́ v path in G. The eccentricity of a vertex v in G is the integer eGpvq “ maxtdGpv, uq : u P VGu. The diameter of G is the integer dpGq “ maxteGpvq : v P VGu. The periphery of a vertex v of G is the set PGpvq “ tu P VG : dGpv, uq “ eGpvqu, while the periphery of G is the set P pGq “ tv P VG : eGpvq “ dpGqu. We say that graph G is hangable if PGpvq ⊆ P pGq for every vertex v of G. In this paper we prove that every block graph is hangable and discuss the hangability of products of graphs.
Cranial defects in the Goldenhar syndrome.

Four patients are presented with the Goldenhar syndrome (GS) and cranial defects consisting of plagiocephaly, microcephaly, skull defects, or intracranial dermoid cysts. Twelve cases from the literature add hydrocephalus, encephalocele, and arhinencephaly to a growing list of brain anomalies in GS. As a group, these patients emphasize the variability of GS and the increased risk for developmental retardation with multiple, severe, or unusual manifestations. The temporal relation of proposed teratogenic events in GS provides an opportunity to reconstruct biological relationships within the 3-5-week human embryo.
M2M Service Platforms: Survey, Issues, and Enabling Technologies

Machine-to-Machine (M2M) refers to technologies with various applications. In order to provide the vision and goals of M2M, an M2M ecosystem with a service platform must be established by the key players in industrial domains so as to substantially reduce development costs and improve time to market of M2M devices and services. The service platform must be supported by M2M enabling technologies and standardization. In this paper, we present a survey of existing M2M service platforms and explore the various research issues and challenges involved in enabling an M2M service platform. We first classify M2M nodes according to their characteristics and required functions, and we then highlight the features of M2M traffic. With these in mind, we discuss the necessity of M2M platforms. By comparing and analyzing the existing approaches and solutions of M2M platforms, we identify the requirements and functionalities of the ideal M2M service platform. Based on these, we propose an M2M service platform (M2SP) architecture and its functionalities, and present the M2M ecosystem with this platform. Different application scenarios are given to illustrate the interaction between the components of the proposed platform. In addition, we discuss the issues and challenges of enabling technologies and standardization activities, and outline future research directions for the M2M network.